Model driven state machine transitions to configure an installation of a software program

ABSTRACT

Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.17/163,135, filed Jan. 29, 2021, which application is incorporated byreference herein in its entirety.

RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are incorporated by reference under 37 CFR 1.57 and made apart of this specification.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”). The machine data can include log data, performancedata, diagnostic data, metrics, tracing data, or any other data that canbe analyzed to diagnose equipment performance problems, monitor userinteractions, and to derive other insights.

The large amount and diversity of data systems containing large amountsof structured, semi-structured, and unstructured data relevant to anysearch query can be massive, and continues to grow rapidly. Thistechnological evolution can give rise to various challenges in relationto managing, understanding, and effectively utilizing the data. Toreduce the potentially vast amount of data that may be generated, somedata systems pre-process data based on anticipated data analysis needs.In particular, specified data items may be extracted from the generateddata and stored in a data system to facilitate efficient retrieval andanalysis of those data items at a later time. At least some of theremainder of the generated data is typically discarded duringpre-processing.

However, storing massive quantities of minimally processed orunprocessed data (collectively and individually referred to as “rawdata”) for later retrieval and analysis is becoming increasingly morefeasible as storage capacity becomes more inexpensive and plentiful. Ingeneral, storing raw data and performing analysis on that data later canprovide greater flexibility because it enables an analyst to analyze allof the generated data instead of only a fraction of it. Although theavailability of vastly greater amounts of diverse data on diverse datasystems provides opportunities to derive new insights, it also givesrise to technical challenges to search and analyze the data in aperformant way.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative examples are described in detail below with reference tothe following figures:

FIG. 1 is a block diagram of an embodiment of a data processingenvironment.

FIG. 2 is a flow diagram illustrating an embodiment of a routineimplemented by the data intake and query system to process, index, andstore data.

FIG. 3A is a block diagram illustrating an embodiment of machine datareceived by the data intake and query system.

FIGS. 3B and 3C are block diagrams illustrating embodiments of variousdata structures for storing data processed by the data intake and querysystem.

FIG. 4A is a flow diagram illustrating an embodiment of a routineimplemented by the query system to execute a query.

FIG. 4B provides a visual representation of the manner in which apipelined command language or query can operate

FIG. 4C is a block diagram illustrating an embodiment of a configurationfile that includes various extraction rules that can be applied toevents.

FIG. 4D is a block diagram illustrating an example scenario where acommon customer identifier is found among log data received fromdisparate data sources.

FIG. 5 is an overview diagram of a computing system implementing one ormore of the disclosed embodiments.

FIG. 6 is a state transition diagram illustrating example statetransitions implemented in one or more of the disclosed embodiments.

FIG. 7 shows an example machine learning module according to someexamples of the present disclosure.

FIG. 8 illustrates data flow during a model training process that isimplemented in one or more of the disclosed embodiments.

FIG. 9 illustrates data flow during a model training process that isimplemented in one or more of the disclosed embodiments.

FIG. 10A illustrates data flow through a model in one or more of thedisclosed embodiments.

FIG. 10B illustrates a mapping from a plurality of predefined states1065 to configurations defining or associated with operational parametervalues.

FIG. 11 is a flowchart of an example method for operating aninstallation of a software program.

FIG. 12 is a flowchart of an example method for training and providing amodel to an installation of a software program.

FIG. 13 illustrates a block diagram of an example machine upon which anyone or more of the techniques (e.g., methodologies) discussed herein mayperform.

DETAILED DESCRIPTION

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine data. Machine data is any data producedby a machine or component in an information technology (IT) environmentand that reflects activity in the IT environment. For example, machinedata can be raw machine data that is generated by various components inIT environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine data can include systemlogs, network packet data, sensor data, application program data, errorlogs, stack traces, system performance data, etc. In general, machinedata can also include performance data, diagnostic information, and manyother types of data that can be analyzed to diagnose performanceproblems, monitor user interactions, and to derive other insights.

A number of tools are available to analyze machine data. In order toreduce the size of the potentially vast amount of machine data that maybe generated, many of these tools typically pre-process the data basedon anticipated data-analysis needs. For example, pre-specified dataitems may be extracted from the machine data and stored in a database tofacilitate efficient retrieval and analysis of those data items atsearch time. However, the rest of the machine data typically is notsaved and is discarded during pre-processing. As storage capacitybecomes progressively cheaper and more plentiful, there are fewerincentives to discard these portions of machine data and many reasons toretain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data),sensor data, application program data, error logs, stack traces, systemperformance data, operating system data, virtualization data) fromthousands of different components, which can collectively be verytime-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices thatconcurrently report these types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE, SPLUNK® CLOUD, orSPLUNK® CLOUD SERVICE system developed by Splunk Inc. of San Francisco,Calif. These systems represent the leading platform for providingreal-time operational intelligence that enables organizations tocollect, index, and search machine data from various websites,applications, servers, networks, and mobile devices that power theirbusinesses. The data intake and query system is particularly useful foranalyzing data which is commonly found in system log files, networkdata, metrics data, tracing data, and other data input sources.

In the data intake and query system, machine data is collected andstored as “events.” An event comprises a portion of machine data and isassociated with a specific point in time. The portion of machine datamay reflect activity in an IT environment and may be produced by acomponent of that IT environment, where the events may be searched toprovide insight into the IT environment, thereby improving theperformance of components in the IT environment. Events may be derivedfrom “time series data,” where the time series data comprises a sequenceof data points (e.g., performance measurements from a computer system)that are associated with successive points in time. In general, eachevent has a portion of machine data that is associated with a timestamp.The time stamp may be derived from the portion of machine data in theevent, determined through interpolation between temporally proximateevents having known timestamps, and/or may be determined based on otherconfigurable rules for associating timestamps with events.

In some instances, machine data can have a predefined structure, wheredata items with specific data formats are stored at predefined locationsin the data. For example, the machine data may include data associatedwith fields in a database table. In other instances, machine data maynot have a predefined structure (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data can comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event can include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The machine data generated bysuch data sources can include, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements, sensor measurements, etc.

The data intake and query system can use flexible schema to specify howto extract information from events. A flexible schema may be developedand redefined as needed. The flexible schema can be applied to events“on the fly,” when it is needed (e.g., at search time, index time,ingestion time, etc.). When the schema is not applied to events untilsearch time, the schema may be referred to as a “late-binding schema.”

During operation, the data intake and query system receives machine datafrom any type and number of sources (e.g., one or more system logs,streams of network packet data, sensor data, application program data,error logs, stack traces, system performance data, etc.). The systemparses the machine data to produce events each having a portion ofmachine data associated with a timestamp, and stores the events. Thesystem enables users to run queries against the stored events to, forexample, retrieve events that meet filter criteria specified in a query,such as criteria indicating certain keywords or having specific valuesin defined fields. Additional query terms can further process the eventdata, such as, by transforming the data, etc.

As used herein, the term “field” can refer to a location in the machinedata of an event containing one or more values for a specific data item.A field may be referenced by a field name associated with the field. Aswill be described in more detail herein, in some cases, a field isdefined by an extraction rule (e.g., a regular expression) that derivesone or more values or a sub-portion of text from the portion of machinedata in each event to produce a value for the field for that event. Theset of values produced are semantically-related (such as IP address),even though the machine data in each event may be in different formats(e.g., semantically-related values may be in different positions in theevents derived from different sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration filecan include one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system can use one or more configuration files todetermine whether there is an extraction rule for that particular fieldname that applies to each event that falls within the criteria of thesearch. If so, the event is considered as part of the search results(and additional processing may be performed on that event based oncriteria specified in the search). If not, the next event is similarlyanalyzed, and so on.

As noted above, the data intake and query system can utilize alate-binding schema while performing queries on events. One aspect of alate-binding schema is applying extraction rules to events to extractvalues for specific fields during search time. More specifically, theextraction rule for a field can include one or more instructions thatspecify how to extract a value for the field from an event. Anextraction rule can generally include any type of instruction forextracting values from machine data or events. In some cases, anextraction rule comprises a regular expression, where a sequence ofcharacters form a search pattern. An extraction rule comprising aregular expression is referred to herein as a regex rule. The systemapplies a regex rule to machine data or an event to extract values for afield associated with the regex rule, where the values are extracted bysearching the machine data/event for the sequence of characters definedin the regex rule.

In the data intake and query system, a field extractor may be configuredto automatically generate extraction rules for certain fields in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields specified in aquery may be provided in the query itself, or may be located duringexecution of the query. Hence, as a user learns more about the data inthe events, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules for use the next time the schema is used by the system. Becausethe data intake and query system maintains the underlying machine dataand uses a late-binding schema for searching the machine data, itenables a user to continue investigating and learn valuable insightsabout the machine data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent and/or similar data items, even thoughthe fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources.

In some embodiments, the configuration files and/or extraction rulesdescribed above can be stored in a catalog, such as a metadata catalog.In certain embodiments, the content of the extraction rules can bestored as rules or actions in the metadata catalog. For example, theidentification of the data to which the extraction rule applies can bereferred to a rule and the processing of the data can be referred to asan action.

1.0. Operating Environment

FIG. 1 is a block diagram of an embodiment of a data processingenvironment 100. In the illustrated embodiment, the environment 100includes a data intake and query system 102, one or more host devices104, and one or more client computing devices 106 (generically referredto as client device(s) 106).

The data intake and query system 102, host devices 104, and clientdevices 106 can communicate with each other via one or more networks,such as a local area network (LAN), wide area network (WAN), private orpersonal network, cellular networks, intranetworks, and/or internetworksusing any of wired, wireless, terrestrial microwave, satellite links,etc., and may include the Internet. Although not explicitly shown inFIG. 1 , it will be understood that a client computing device 106 cancommunicate with a host device 104 via one or more networks. Forexample, if the host device 104 is configured as a web server and theclient computing device 106 is a laptop, the laptop can communicate withthe web server to view a website.

A client device 106 can correspond to a distinct computing device thatcan configure, manage, or sends queries to the system 102. Examples ofclient devices 106 may include, without limitation, smart phones, tabletcomputers, handheld computers, wearable devices, laptop computers,desktop computers, servers, portable media players, gaming devices, orother device that includes computer hardware (e.g., processors,non-transitory, computer-readable media) and so forth. In certain cases,a client device 106 can include a hosted, virtualized, or containerizeddevice, such as an isolated execution environment, that shares computingresources (e.g., processor, memory) of a particular machine with otherisolated execution environments.

The client devices 106 can interact with the system 102 (or a hostdevice 104) in a variety of ways. For example, the client devices 106can communicate with the system 102 (or a host device 104) over anInternet (Web) protocol, via a gateway, via a command line interface,via a software developer kit (SDK), a standalone application, etc. Asanother example, the client devices 106 can use one or more executableapplications or programs to interface with the system 102.

A host device 104 can correspond to a distinct computing device orsystem that includes or has access to data that can be ingested,indexed, and/or searched by the system 102. Accordingly, in some cases,a client device 106 may also be a host device 104 (e.g., it can includedata that is ingested by the system 102 and it can submit queries to thesystem 102). The host devices 104 can include, but are not limited to,servers, sensors, routers, personal computers, mobile devices, internetof things (TOT) devices, or hosting devices, such as computing devicesin a shared computing resource environment on which multiple isolatedexecution environment (e.g., virtual machines, containers, etc.) can beinstantiated, or other computing devices in an IT environment (e.g.,device that includes computer hardware, e.g., processors,non-transitory, computer-readable media, etc.). In certain cases, a hostdevice 104 can include a hosted, virtualized, or containerized device,such as an isolated execution environment, that shares computingresources (e.g., processor, memory, etc.) of a particular machine (e.g.,a hosting device or hosting machine) with other isolated executionenvironments.

As mentioned host devices 104 can include or have access to data sourcesfor the system 102. The data sources can include machine data found inlog files, data files, distributed file systems, streaming data,publication-subscribe (pub/sub) buffers, directories of files, data sentover a network, event logs, registries, streaming data services(examples of which can include, by way of non-limiting example, Amazon'sSimple Queue Service (“SQS”) or Kinesis™ services, devices executingApache Kafka™ software, or devices implementing the Message QueueTelemetry Transport (MQTT) protocol, Microsoft Azure EventHub, GoogleCloud PubSub, devices implementing the Java Message Service (JMS)protocol, devices implementing the Advanced Message Queuing Protocol(AMQP)), cloud-based services (e.g., AWS, Microsoft Azure, Google Cloud,etc.), operating-system-level virtualization environments (e.g.,Docker), container orchestration systems (e.g., Kubernetes), virtualmachines using full virtualization or paravirtualization, or othervirtualization technique or isolated execution environments.

In some cases, one or more applications executing on a host device maygenerate various types of machine data during operation. For example, aweb server application executing on a host device 104 may generate oneor more web server logs detailing interactions between the web serverand any number of client devices 106 or other devices. As anotherexample, a host device 104 implemented as a router may generate one ormore router logs that record information related to network trafficmanaged by the router. As yet another example, a database serverapplication executing on a host device 104 may generate one or more logsthat record information related to requests sent from other devices(e.g., web servers, application servers, client devices, etc.) for datamanaged by the database server. Similarly, a host device 104 maygenerate and/or store computing resource utilization metrics, such as,but not limited to, CPU utilization, memory utilization, number ofprocesses being executed, etc. Any one or any combination of the filesor data generated in such cases can be used as a data source for thesystem 102.

In some embodiments, an application may include a monitoring componentthat facilitates generating performance data related to host device'soperating state, including monitoring network traffic sent and receivedfrom the host device and collecting other device and/orapplication-specific information. A monitoring component may be anintegrated component of the application, a plug-in, an extension, or anyother type of add-on component, or a stand-alone process.

Such monitored information may include, but is not limited to, networkperformance data (e.g., a URL requested, a connection type (e.g., HTTP,HTTPS, etc.), a connection start time, a connection end time, an HTTPstatus code, request length, response length, request headers, responseheaders, connection status (e.g., completion, response time(s), failure,etc.)) or device performance information (e.g., current wireless signalstrength of the device, a current connection type and network carrier,current memory performance information, processor utilization, memoryutilization, a geographic location of the device, a device orientation,and any other information related to the operational state of the hostdevice, etc.), device profile information (e.g., a type of clientdevice, a manufacturer, and model of the device, versions of varioussoftware applications installed on the device, etc.) In some cases, themonitoring component can collect device performance information bymonitoring one or more host device operations, or by making calls to anoperating system and/or one or more other applications executing on ahost device for performance information. The monitored information maybe stored in one or more files and/or streamed to the system 102.

In general, a monitoring component may be configured to generateperformance data in response to a monitor trigger in the code of aclient application or other triggering application event, as describedabove, and to store the performance data in one or more data records.Each data record, for example, may include a collection of field-valuepairs, each field-value pair storing a particular item of performancedata in association with a field for the item. For example, a datarecord generated by a monitoring component may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

In some embodiments, such as in a shared computing resource environment(or hosted environment), a host device 104 may include logs or machinedata generated by an application executing within an isolated executionenvironment (e.g., web server log file if the isolated executionenvironment is configured as a web server or database server log filesif the isolated execution environment is configured as database server,etc.), machine data associated with the computing resources assigned tothe isolated execution environment (e.g., CPU utilization of the portionof the CPU allocated to the isolated execution environment, memoryutilization of the portion of the memory allocated to the isolatedexecution environment, etc.), logs or machine data generated by anapplication that enables the isolated execution environment to shareresources with other isolated execution environments (e.g., logsgenerated by a Docker manager or Kubernetes manager executing on thehost device 104), and/or machine data generated by monitoring thecomputing resources of the host device 104 (e.g., CPU utilization,memory utilization, etc.) that are shared between the isolated executionenvironments. Given the separation (and isolation) between isolatedexecution environments executing on a common computing device, incertain embodiments, each isolated execution environment may be treatedas a separate host device 104 even if they are, in fact, executing onthe same computing device or hosting device.

Accordingly, as used herein, obtaining data from a data source may referto communicating with a host device 104 to obtain data from the hostdevice 104 (e.g., from one or more data source files, data streams,directories on the host device 104, etc.). For example, obtaining datafrom a data source may refer to requesting data from a host device 104and/or receiving data from a host device 104. In some such cases, thehost device 104 can retrieve and return the requested data from aparticular data source and/or the system 102 can retrieve the data froma particular data source of the host device 104 (e.g., from a particularfile stored on a host device 104).

The data intake and query system 102 can ingest, index, and/or storedata from heterogeneous data sources and/or host devices 104. Forexample, the system 102 can ingest, index, and/or store any type ofmachine data, regardless of the form of the machine data or whether themachine data matches or is similar to other machine data ingested,indexed, and/or stored by the system 102. In some cases, the system 102can generate events from the received data, group the events, and storethe events in buckets. The system 102 can also search heterogeneous datathat it has stored or search data stored by other systems (e.g., othersystem 102 systems or other non-system 102 systems). For example, inresponse to received queries, the system 102 can assign one or morecomponents to search events stored in the storage system or search datastored elsewhere.

As will be described herein in greater detail below, the system 102 canuse one or more components to ingest, index, store, and/or search data.In some embodiments, the system 102 is implemented as a distributedsystem that uses multiple components to perform its various functions.For example, the system 102 can include any one or any combination of anintake system 110 (including one or more components) to ingest data, anindexing system 112 (including one or more components) to index thedata, a storage system 116 (including one or more components) to storethe data, and/or a query system 114 (including one or more components)to search the data, etc.

In the illustrated embodiment, the system 102 is shown having foursubsystems 110, 112, 114, 116. However, it will be understood that thesystem 102 may include any one or any combination of the intake system110, indexing system 112, query system 114, or storage system 116.Further, in certain embodiments, one or more of the intake system 110,indexing system 112, query system 114, or storage system 116 may be usedalone or apart from the system 102. For example, the intake system 110may be used alone to glean information from streaming data that is notindexed or stored by the system 102, or the query system 114 may be usedto search data that is unaffiliated with the system 102.

In certain embodiments, the components of the different systems may bedistinct from each other or there may be some overlap. For example, onecomponent of the system 102 may include some indexing functionality andsome searching functionality and thus be used as part of the indexingsystem 112 and query system 114, while another computing device of thesystem 102 may only have ingesting or search functionality and only beused as part of those respective systems. Similarly, the components ofthe storage system 116 may include data stores of individual componentsof the indexing system and/or may be a separate shared data storagesystem, like Amazon S3, that is accessible to distinct components of theintake system 110, indexing system 112, and query system 114.

In some cases, the components of the system 102 are implemented asdistinct computing devices having their own computer hardware (e.g.,processors, non-transitory, computer-readable media, etc.) and/or asdistinct hosted devices (e.g., isolated execution environments) thatshare computing resources or hardware in a shared computing resourceenvironment.

For simplicity, references made herein to the intake system 110,indexing system 112, storage system 116, and query system 114 can referto those components used for ingesting, indexing, storing, andsearching, respectively. However, it will be understood that althoughreference is made to two separate systems, the same underlying componentmay be performing the functions for the two different systems. Forexample, reference to the indexing system indexing data and storing thedata in the storage system 116 or the query system searching the datamay refer to the same component (e.g., same computing device or hosteddevice) indexing the data, storing the data, and then searching the datathat it stored.

As will be described in greater detail herein, the intake system 110 canreceive data from the host devices 104 or data sources, perform one ormore preliminary processing operations on the data, and communicate thedata to the indexing system 112, query system 114, storage system 116,or to other systems (which may include, for example, data processingsystems, telemetry systems, real-time analytics systems, data stores,databases, etc., any of which may be operated by an operator of thesystem 102 or a third party). Given the amount of data that can beingested by the intake system 110, in some embodiments, the intakesystem can include multiple distributed computing devices or componentsworking concurrently to ingest the data.

The intake system 110 can receive data from the host devices 104 in avariety of formats or structures. In some embodiments, the received datacorresponds to raw machine data, structured or unstructured data,correlation data, data files, directories of files, data sent over anetwork, event logs, registries, messages published to streaming datasources, performance metrics, sensor data, image and video data, etc.

The preliminary processing operations performed by the intake system 110can include, but is not limited to, associating metadata with the datareceived from a host device 104, extracting a timestamp from the data,identifying individual events within the data, extracting a subset ofmachine data for transmittal to the indexing system 112, enriching thedata, etc. As part of communicating the data to the indexing system, theintake system 110 can route the data to a particular component of theintake system 110 or dynamically route the data based on load-balancing,etc. In certain cases, one or more components of the intake system 110can be installed on a host device 104.

1.4.2. Indexing System Overview

As will be described in greater detail herein, the indexing system 112can include one or more components (e.g., indexing nodes) to process thedata and store it, for example, in the storage system 116. As part ofprocessing the data, the indexing system can identify distinct eventswithin the data, timestamps associated with the data, organize the datainto buckets or time series buckets, convert editable buckets tonon-editable buckets, store copies of the buckets in the storage system116, merge buckets, generate indexes of the data, etc. In addition, theindexing system 112 can update various catalogs or databases withinformation related to the buckets (pre-merged or merged) or data thatis stored in the storage system 116, and can communicate with the intakesystem 110 about the status of the data storage.

As will be described in greater detail herein, the query system 114 caninclude one or more components to receive, process, and execute queries.In some cases, the query system 114 can use the same component toprocess and execute the query or use one or more components to receiveand process the query (e.g., a search head) and use one or more othercomponents to execute at least a portion of the query (e.g., searchnodes). In some cases, a search node and an indexing node may refer tothe same computing device or hosted device performing differentfunctions. In certain cases, a search node can be a separate computingdevice or hosted device from an indexing node.

Queries received by the query system 114 can be relatively complex andidentify a set of data to be processed and a manner of processing theset of data from one or more client devices 106. In certain cases, thequery can be implemented using a pipelined command language or otherquery language. As described herein, in some cases, the query system 114can execute parts of the query in a distributed fashion (e.g., one ormore mapping phases or parts associated with identifying and gatheringthe set of data identified in the query) and execute other parts of thequery on a single component (e.g., one or more reduction phases).However, it will be understood that in some cases multiple componentscan be used in the map and/or reduce functions of the query execution.

In some cases, as part of executing the query, the query system 114 canuse one or more catalogs or databases to identify the set of data to beprocessed or its location in the storage system 116 and/or can retrievedata from the storage system 116. In addition, in some embodiments, thequery system 114 can store some or all of the query results in thestorage system 116.

In some cases, the storage system 116 may include one or more datastores associated with or coupled to the components of the indexingsystem 112 that are accessible via a system bus or local area network.In certain embodiments, the storage system 116 may be a shared storagesystem 116, like Amazon S3 or Google Cloud Storage, that are accessiblevia a wide area network.

As mentioned and as will be described in greater detail below, thestorage system 116 can be made up of one or more data stores storingdata that has been processed by the indexing system 112. In some cases,the storage system includes data stores of the components of theindexing system 112 and/or query system 114. In certain embodiments, thestorage system 116 can be implemented as a shared storage system 116.The shared storage system 116 can be configured to provide highavailability, highly resilient, low loss data storage. In some cases, toprovide the high availability, highly resilient, low loss data storage,the shared storage system 116 can store multiple copies of the data inthe same and different geographic locations and across different typesof data stores (e.g., solid state, hard drive, tape, etc.). Further, asdata is received at the shared storage system 116 it can beautomatically replicated multiple times according to a replicationfactor to different data stores across the same and/or differentgeographic locations. In some embodiments, the shared storage system 116can correspond to cloud storage, such as Amazon Simple Storage Service(S3) or Elastic Block Storage (EBS), Google Cloud Storage, MicrosoftAzure Storage, etc.

In some embodiments, indexing system 112 can read to and write from theshared storage system 116. For example, the indexing system 112 can copybuckets of data from its local or shared data stores to the sharedstorage system 116. In certain embodiments, the query system 114 canread from, but cannot write to, the shared storage system 116. Forexample, the query system 114 can read the buckets of data stored inshared storage system 116 by the indexing system 112, but may not beable to copy buckets or other data to the shared storage system 116. Insome embodiments, the intake system 110 does not have access to theshared storage system 116. However, in some embodiments, one or morecomponents of the intake system 110 can write data to the shared storagesystem 116 that can be read by the indexing system 112.

As described herein, in some embodiments, data in the system 102 (e.g.,in the data stores of the components of the indexing system 112, sharedstorage system 116, or search nodes of the query system 114) can bestored in one or more time series buckets. Each bucket can include rawmachine data associated with a timestamp and additional informationabout the data or bucket, such as, but not limited to, one or morefilters, indexes (e.g., TSIDX, inverted indexes, keyword indexes, etc.),bucket summaries, etc. In some embodiments, the bucket data andinformation about the bucket data is stored in one or more files. Forexample, the raw machine data, filters, indexes, bucket summaries, etc.can be stored in respective files in or associated with a bucket. Incertain cases, the group of files can be associated together to form thebucket.

The system 102 can include additional components that interact with anyone or any combination of the intake system 110, indexing system 112,query system 114, and/or storage system 116. Such components mayinclude, but are not limited to an authentication system, orchestrationsystem, one or more catalogs or databases, a gateway, etc.

An authentication system can include one or more components toauthenticate users to access, use, and/or configure the system 102.Similarly, the authentication system can be used to restrict what aparticular user can do on the system 102 and/or what components or dataa user can access, etc.

An orchestration system can include one or more components to manageand/or monitor the various components of the system 102. In someembodiments, the orchestration system can monitor the components of thesystem 102 to detect when one or more components has failed or isunavailable and enable the system 102 to recover from the failure (e.g.,by adding additional components, fixing the failed component, or havingother components complete the tasks assigned to the failed component).In certain cases, the orchestration system can determine when to addcomponents to or remove components from a particular system 110, 112,114, 116 (e.g., based on usage, user/tenant requests, etc.). Inembodiments where the system 102 is implemented in a shared computingresource environment, the orchestration system can facilitate thecreation and/or destruction of isolated execution environments orinstances of the components of the system 102, etc.

In certain embodiments, the system 102 can include various componentsthat enable it to provide stateless services or enable it to recoverfrom an unavailable or unresponsive component without data loss in atime efficient manner. For example, the system 102 can store contextualinformation about its various components in a distributed way such thatif one of the components becomes unresponsive or unavailable, the system102 can replace the unavailable component with a different component andprovide the replacement component with the contextual information. Inthis way, the system 102 can quickly recover from an unresponsive orunavailable component while reducing or eliminating the loss of datathat was being processed by the unavailable component.

In some embodiments, the system 102 can store the contextual informationin a catalog, as described herein. In certain embodiments, thecontextual information can correspond to information that the system 102has determined or learned based on use. In some cases, the contextualinformation can be stored as annotations (manual annotations and/orsystem annotations), as described herein.

In certain embodiments, the system 102 can include an additional catalogthat monitors the location and storage of data in the storage system 116to facilitate efficient access of the data during search time. Incertain embodiments, such a catalog may form part of the storage system116.

In some embodiments, the system 102 can include a gateway or othermechanism to interact with external devices or to facilitatecommunications between components of the system 102. In someembodiments, the gateway can be implemented using an applicationprogramming interface (API). In certain embodiments, the gateway can beimplemented using a representational state transfer API (REST API).

In some environments, a user of a system 102 may install and configure,on computing devices owned and operated by the user, one or moresoftware applications that implement some or all of the components ofthe system 102. For example, with reference to FIG. 1 , a user mayinstall a software application on server computers owned by the user andconfigure each server to operate as one or more components of the intakesystem 110, indexing system 112, query system 114, shared storage system116, or other components of the system 102. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 102is installed and operates on computing devices directly controlled bythe user of the system 102. Some users may prefer an on-premisessolution because it may provide a greater level of control over theconfiguration of certain aspects of the system (e.g., security, privacy,standards, controls, etc.). However, other users may instead prefer anarrangement in which the user is not directly responsible for providingand managing the computing devices upon which various components ofsystem 102 operate.

In certain embodiments, one or more of the components of the system 102can be implemented in a shared computing resource environment. In thiscontext, a shared computing resource environment or cloud-based servicecan refer to a service hosted by one more computing resources that areaccessible to end users over a network, for example, by using a webbrowser or other application on a client device to interface with theremote computing resources. For example, a service provider may providea system 102 by managing computing resources configured to implementvarious aspects of the system (e.g., intake system 110, indexing system112, query system 114, shared storage system 116, other components,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

When implemented in a shared computing resource environment, theunderlying hardware (non-limiting examples: processors, hard drives,solid-state memory, RANI, etc.) on which the components of the system102 execute can be shared by multiple customers or tenants as part ofthe shared computing resource environment. In addition, when implementedin a shared computing resource environment as a cloud-based service,various components of the system 102 can be implemented usingcontainerization or operating-system-level virtualization, or othervirtualization technique. For example, one or more components of theintake system 110, indexing system 112, or query system 114 can beimplemented as separate software containers or container instances. Eachcontainer instance can have certain computing resources (e.g., memory,processor, etc.) of an underlying hosting computing system (e.g.,server, microprocessor, etc.) assigned to it, but may share the sameoperating system and may use the operating system's system callinterface. Each container may provide an isolated execution environmenton the host system, such as by providing a memory space of the hostingsystem that is logically isolated from memory space of other containers.Further, each container may run the same or different computerapplications concurrently or separately, and may interact with eachother. Although reference is made herein to containerization andcontainer instances, it will be understood that other virtualizationtechniques can be used. For example, the components can be implementedusing virtual machines using full virtualization or paravirtualization,etc. Thus, where reference is made to “containerized” components, itshould be understood that such components may additionally oralternatively be implemented in other isolated execution environments,such as a virtual machine environment.

Implementing the system 102 in a shared computing resource environmentcan provide a number of benefits. In some cases, implementing the system102 in a shared computing resource environment can make it easier toinstall, maintain, and update the components of the system 102. Forexample, rather than accessing designated hardware at a particularlocation to install or provide a component of the system 102, acomponent can be remotely instantiated or updated as desired. Similarly,implementing the system 102 in a shared computing resource environmentor as a cloud-based service can make it easier to meet dynamic demand.For example, if the system 102 experiences significant load at indexingor search, additional compute resources can be deployed to process theadditional data or queries. In an “on-premises” environment, this typeof flexibility and scalability may not be possible or feasible.

In addition, by implementing the system 102 in a shared computingresource environment or as a cloud-based service can improve computeresource utilization. For example, in an on-premises environment if thedesignated compute resources are not being used by, they may sit idleand unused. In a shared computing resource environment, if the computeresources for a particular component are not being used, they can bere-allocated to other tasks within the system 102 and/or to othersystems unrelated to the system 102.

As mentioned, in an on-premises environment, data from one instance of asystem 102 is logically and physically separated from the data ofanother instance of a system 102 by virtue of each instance having itsown designated hardware. As such, data from different customers of thesystem 102 is logically and physically separated from each other. In ashared computing resource environment, components of a system 102 can beconfigured to process the data from one customer or tenant or frommultiple customers or tenants. Even in cases where a separate componentof a system 102 is used for each customer, the underlying hardware onwhich the components of the system 102 are instantiated may stillprocess data from different tenants. Accordingly, in a shared computingresource environment, the data from different tenants may not bephysically separated on distinct hardware devices. For example, datafrom one tenant may reside on the same hard drive as data from anothertenant or be processed by the same processor. In such cases, the system102 can maintain logical separation between tenant data. For example,the system 102 can include separate directories for different tenantsand apply different permissions and access controls to access thedifferent directories or to process the data, etc.

In certain cases, the tenant data from different tenants is mutuallyexclusive and/or independent from each other. For example, in certaincases, Tenant A and Tenant B do not share the same data, similar to theway in which data from a local hard drive of Customer A is mutuallyexclusive and independent of the data (and not considered part) of alocal hard drive of Customer B. While Tenant A and Tenant B may havematching or identical data, each tenant would have a separate copy ofthe data. For example, with reference again to the local hard drive ofCustomer A and Customer B example, each hard drive could include thesame file. However, each instance of the file would be considered partof the separate hard drive and would be independent of the other file.Thus, one copy of the file would be part of Customer's A hard drive anda separate copy of the file would be part of Customer B's hard drive. Ina similar manner, to the extent Tenant A has a file that is identical toa file of Tenant B, each tenant would have a distinct and independentcopy of the file stored in different locations on a data store or ondifferent data stores.

Further, in certain cases, the system 102 can maintain the mutualexclusivity and/or independence between tenant data even as the tenantdata is being processed, stored, and searched by the same underlyinghardware. In certain cases, to maintain the mutual exclusivity and/orindependence between the data of different tenants, the system 102 canuse tenant identifiers to uniquely identify data associated withdifferent tenants.

In a shared computing resource environment, some components of thesystem 102 can be instantiated and designated for individual tenants andother components can be shared by multiple tenants. In certainembodiments, a separate intake system 110, indexing system 112, andquery system 114 can be instantiated for each tenant, whereas the sharedstorage system 116 or other components (e.g., data store, metadatacatalog, and/or acceleration data store, described below) can be sharedby multiple tenants. In some such embodiments where components areshared by multiple tenants, the components can maintain separatedirectories for the different tenants to ensure their mutual exclusivityand/or independence from each other. Similarly, in some suchembodiments, the system 102 can use different hosting computing systemsor different isolated execution environments to process the data fromthe different tenants as part of the intake system 110, indexing system112, and/or query system 114.

In some embodiments, individual components of the intake system 110,indexing system 112, and/or query system 114 may be instantiated foreach tenant or shared by multiple tenants. For example, some individualintake system components (e.g., forwarders, output ingestion buffer) maybe instantiated and designated for individual tenants, while otherintake system components (e.g., a data retrieval subsystem, intakeingestion buffer, and/or streaming data processor), may be shared bymultiple tenants.

In certain embodiments, an indexing system 112 (or certain componentsthereof) can be instantiated and designated for a particular tenant orshared by multiple tenants. In some embodiments where a separateindexing system 112 is instantiated and designated for each tenant,different resources can be reserved for different tenants. For example,Tenant A can be consistently allocated a minimum of four indexing nodesand Tenant B can be consistently allocated a minimum of two indexingnodes. In some such embodiments, the four indexing nodes can be reservedfor Tenant A and the two indexing nodes can be reserved for Tenant B,even if Tenant A and Tenant B are not using the reserved indexing nodes.

In embodiments where an indexing system 112 is shared by multipletenants, components of the indexing system 112 can be dynamicallyassigned to different tenants. For example, if Tenant A has greaterindexing demands, additional indexing nodes can be instantiated orassigned to Tenant A's data. However, as the demand decreases, theindexing nodes can be reassigned to a different tenant, or terminated.Further, in some embodiments, a component of the indexing system 112 canconcurrently process data from the different tenants.

In some embodiments, one instance of query system 114 may be shared bymultiple tenants. In some such cases, the same search head can be usedto process/execute queries for different tenants and/or the same searchnodes can be used to execute query for different tenants. Further, insome such cases, different tenants can be allocated different amounts ofcompute resources. For example, Tenant A may be assigned more searchheads or search nodes based on demand or based on a service levelarrangement than another tenant. However, once a search is completed thesearch head and/or nodes assigned to Tenant A may be assigned to TenantB, deactivated, or their resource may be re-allocated to othercomponents of the system 102, etc.

In some cases, by sharing more components with different tenants, thefunctioning of the system 102 can be improved. For example, by sharingcomponents across tenants, the system 102 can improve resourceutilization thereby reducing the amount of resources allocated as awhole. For example, if four indexing nodes, two search heads, and foursearch nodes are reserved for each tenant then those compute resourcesare unavailable for use by other processes or tenants, even if they gounused. In contrast, by sharing the indexing nodes, search heads, andsearch nodes with different tenants and instantiating additional computeresources, the system 102 can use fewer resources overall whileproviding improved processing time for the tenants that are using thecompute resources. For example, if tenant A is not using any searchnodes and tenant B has many searches running, the system 102 can usesearch nodes that would have been reserved for tenant A to servicetenant B. In this way, the system 102 can decrease the number of computeresources used/reserved, while improving the search time for tenant Band improving compute resource utilization.

2.0. Data Ingestion, Indexing, and Storage

FIG. 2 is a flow diagram illustrating an embodiment of a routineimplemented by the system 102 to process, index, and store data receivedfrom host devices 104. The data flow illustrated in FIG. 2 is providedfor illustrative purposes only. It will be understood that one or moreof the steps of the processes illustrated in FIG. 2 may be removed orthat the ordering of the steps may be changed. Furthermore, for thepurposes of illustrating a clear example, one or more particular systemcomponents are described in the context of performing various operationsduring each of the data flow stages. For example, the intake system 110is described as receiving machine data and the indexing system 112 isdescribed as generating events, grouping events, and storing events.However, other system arrangements and distributions of the processingsteps across system components may be used. For example, in some cases,the intake system 110 may generate events.

At block 202, the intake system 110 receives data from a host device104. The intake system 110 initially may receive the data as a raw datastream generated by the host device 104. For example, the intake system110 may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. Non-limiting examples of machine datathat can be received by the intake system 110 is described herein withreference to FIG. 3A.

In some embodiments, the intake system 110 receives the raw data and maysegment the data stream into messages, possibly of a uniform data size,to facilitate subsequent processing steps. The intake system 110 maythereafter process the messages in accordance with one or more rules toconduct preliminary processing of the data. In one embodiment, theprocessing conducted by the intake system 110 may be used to indicateone or more metadata fields applicable to each message. For example, theintake system 110 may include metadata fields within the messages, orpublish the messages to topics indicative of a metadata field. Thesemetadata fields may, for example, provide information related to amessage as a whole and may apply to each event that is subsequentlyderived from the data in the message. For example, the metadata fieldsmay include separate fields specifying each of a host, a source, and asourcetype related to the message. A host field may contain a valueidentifying a host name or IP address of a device that generated thedata. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A sourcetype field may contain a value specifyinga particular sourcetype label for the data. Additional metadata fieldsmay also be included, such as a character encoding of the data, ifknown, and possibly other values that provide information relevant tolater processing steps. In certain embodiments, the intake system 110may perform additional operations, such as, but not limited to,identifying individual events within the data, determining timestampsfor the data, further enriching the data, etc.

At block 204, the indexing system 112 generates events from the data. Insome cases, as part of generating the events, the indexing system 112can parse the data of the message. In some embodiments, the indexingsystem 112 can determine a sourcetype associated with each message(e.g., by extracting a sourcetype label from the metadata fieldsassociated with the message, etc.) and refer to a sourcetypeconfiguration corresponding to the identified sourcetype to parse thedata of the message. The sourcetype definition may include one or moreproperties that indicate to the indexing system 112 to automaticallydetermine the boundaries within the received data that indicate theportions of machine data for events. In general, these properties mayinclude regular expression-based rules or delimiter rules where, forexample, event boundaries may be indicated by predefined characters orcharacter strings. These predefined characters may include punctuationmarks or other special characters including, for example, carriagereturns, tabs, spaces, line breaks, etc. If a sourcetype for the data isunknown to the indexing system 112, the indexing system 112 may infer asourcetype for the data by examining the structure of the data. Then,the indexing system 112 can apply an inferred sourcetype definition tothe data to create the events.

In addition, as part of generating events from the data, the indexingsystem 112 can determine a timestamp for each event. Similar to theprocess for parsing machine data, the indexing system 112 may againrefer to a sourcetype definition associated with the data to locate oneor more properties that indicate instructions for determining atimestamp for each event. The properties may, for example, instruct theindexing system 112 to extract a time value from a portion of data forthe event (e.g., using a regex rule), to interpolate time values basedon timestamps associated with temporally proximate events, to create atimestamp based on a time the portion of machine data was received orgenerated, to use the timestamp of a previous event, or use any otherrules for determining timestamps, etc.

The indexing system 112 can also associate events with one or moremetadata fields. In some embodiments, a timestamp may be included in themetadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. In certainembodiments, the default metadata fields associated with each event mayinclude a host, source, and sourcetype field including or in addition toa field storing the timestamp.

In certain embodiments, the indexing system 112 can also apply one ormore transformations to event data that is to be included in an event.For example, such transformations can include removing a portion of theevent data (e.g., a portion used to define event boundaries, extraneouscharacters from the event, other extraneous text, etc.), masking aportion of event data (e.g., masking a credit card number), removingredundant portions of event data, etc. The transformations applied toevent data may, for example, be specified in one or more configurationfiles and referenced by one or more sourcetype definitions.

At block 206, the indexing system 112 can group events. In someembodiments, the indexing system 112 can group events based on time. Forexample, events generated within a particular time period or events thathave a time stamp within a particular time period can be groupedtogether to form a bucket. A non-limiting example of a bucket isdescribed herein with reference to FIG. 3B.

In certain embodiments, multiple components of the indexing system, suchas an indexing node, can concurrently generate events and buckets.Furthermore, each indexing node that generates and groups events canconcurrently generate multiple buckets. For example, multiple processorsof an indexing node can concurrently process data, generate events, andgenerate buckets. Further, multiple indexing nodes can concurrentlygenerate events and buckets. As such, ingested data can be processed ina highly distributed manner.

In some embodiments, as part of grouping events together, the indexingsystem 112 can generate one or more inverted indexes for a particulargroup of events. A non-limiting example of an inverted index isdescribed herein with reference to FIG. 3C. In certain embodiments, theinverted indexes can include location information for events of abucket. For example, the events of a bucket may be compressed into oneor more files to reduce their size. The inverted index can includelocation information indicating the particular file and/or locationwithin a particular file of a particular event.

In certain embodiments, the inverted indexes may include keyword entriesor entries for field values or field name-value pairs found in events.In some cases, a field name-value pair can include a pair of wordsconnected by a symbol, such as an equals sign or colon. The entries canalso include location information for events that include the keyword,field value, or field value pair. In this way, relevant events can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2.” In certainembodiments, the indexing system can populate entries in the invertedindex with field name-value pairs by parsing events using one or moreregex rules to determine a field value associated with a field definedby the regex rule. For example, the regex rule may indicate how to finda field value for a userID field in certain events. In some cases, theindexing system 112 can use the sourcetype of the event to determinewhich regex to use for identifying field values.

At block 208, the indexing system 112 stores the events with anassociated timestamp in the storage system 116, which may be in a localdata store and/or in a shared storage system. Timestamps enable a userto search for events based on a time range. In some embodiments, thestored events are organized into “buckets,” where each bucket storesevents associated with a specific time range based on the timestampsassociated with each event. As mentioned, FIGS. 3B and 3C illustrate anexample of a bucket. This improves time-based searching, as well asallows for events with recent timestamps, which may have a higherlikelihood of being accessed, to be stored in a faster memory tofacilitate faster retrieval. For example, buckets containing the mostrecent events can be stored in flash memory rather than on a hard disk.In some embodiments, each bucket may be associated with an identifier, atime range, and a size constraint.

The indexing system 112 may be responsible for storing the events in thestorage system 116. As mentioned, the events or buckets can be storedlocally on a component of the indexing system 112 or in a shared storagesystem 116. In certain embodiments, the component that generates theevents and/or stores the events (indexing node) can also be assigned tosearch the events. In some embodiments separate components can be usedfor generating and storing events (indexing node) and for searching theevents (search node).

By storing events in a distributed manner (either by storing the eventsat different components or in a shared storage system 116), the querysystem 114 can analyze events for a query in parallel. For example,using map-reduce techniques, multiple components of the query system(e.g., indexing or search nodes) can concurrently search and providepartial responses for a subset of events to another component (e.g.,search head) that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, theindexing system 112 may further optimize the data retrieval process bythe query system 114 to search buckets corresponding to time ranges thatare relevant to a query. In some embodiments, each bucket may beassociated with an identifier, a time range, and a size constraint. Incertain embodiments, a bucket can correspond to a file system directoryand the machine data, or events, of a bucket can be stored in one ormore files of the file system directory. The file system directory caninclude additional files, such as one or more inverted indexes, highperformance indexes, permissions files, configuration files, etc.

In embodiments where components of the indexing system 112 store bucketslocally, the components can include a home directory and a colddirectory. The home directory can store hot buckets and warm buckets,and the cold directory stores cold buckets. A hot bucket can refer to abucket that is capable of receiving and storing additional events. Awarm bucket can refer to a bucket that can no longer receive events forstorage, but has not yet been moved to the cold directory. A cold bucketcan refer to a bucket that can no longer receive events and may be abucket that was previously stored in the home directory. The homedirectory may be stored in faster memory, such as flash memory, asevents may be actively written to the home directory, and the homedirectory may typically store events that are more frequently searchedand thus are accessed more frequently. The cold directory may be storedin slower and/or larger memory, such as a hard disk, as events are nolonger being written to the cold directory, and the cold directory maytypically store events that are not as frequently searched and thus areaccessed less frequently. In some embodiments, components of theindexing system 112 may also have a quarantine bucket that containsevents having potentially inaccurate information, such as an incorrecttimestamp associated with the event or a timestamp that appears to be anunreasonable timestamp for the corresponding event. The quarantinebucket may have events from any time range; as such, the quarantinebucket may always be searched at search time. Additionally, componentsof the indexing system may store old, archived data in a frozen bucketthat is not capable of being searched at search time. In someembodiments, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

In some embodiments, components of the indexing system 112 may notinclude a cold directory and/or cold or frozen buckets. For example, inembodiments where buckets are copied to a shared storage system 116 andsearched by separate components of the query system 114, buckets can bedeleted from components of the indexing system as they are stored to thestorage system 116. In certain embodiments, the shared storage system116 may include a home directory that includes warm buckets copied fromthe indexing system 112 and a cold directory of cold or frozen bucketsas described above.

FIG. 3A is a block diagram illustrating an embodiment of machine datareceived by the system 102. The machine data can correspond to data fromone or more host devices 104 or data sources. As mentioned, the datasource can correspond to a log file, data stream or other data structurethat is accessible by a host device 104. In the illustrated embodimentof FIG. 3A, the machine data has different forms. For example, themachine data 302 may be log data that is unstructured or that does nothave any clear structure or fields, and include different portions302A-302E that correspond to different entries of the log and thatseparated by boundaries. Such data may also be referred to as rawmachine data.

The machine data 304 may be referred to as structured or semi-structuredmachine data as it does include some data in a JSON structure definingcertain field and field values (e.g., machine data 304A showing fieldname:field values container_name:kube-apiserver, host:ip 172 20 43173.ec2.internal, pod id:0a73017b-4efa-11e8-a4e1-0a2bf2ab4bba, etc.),but other parts of the machine data 304 is unstructured or raw machinedata (e.g., machine data 304B). The machine data 306 may be referred toas structured data as it includes particular rows and columns of datawith field names and field values.

In some embodiments, the machine data 302 can correspond to log datagenerated by a host device 104 configured as an Apache server, themachine data 304 can correspond to log data generated by a host device104 in a shared computing resource environment, and the machine data 306can correspond to metrics data. Given the differences between hostdevices 104 that generated the log data 302, 304, the form of the logdata 302, 304 is different. In addition, as the log data 304 is from ahost device 104 in a shared computing resource environment, it caninclude log data generated by an application being executed within anisolated execution environment (304B, excluding the field name “log:”)and log data generated by an application that enables the sharing ofcomputing resources between isolated execution environments (all otherdata in 304). Although shown together in FIG. 3A, it will be understoodthat machine data with different hosts, sources, or sourcetypes can bereceived separately and/or found in different data sources and/or hostdevices 104.

As described herein, the system 102 can process the machine data basedon the form in which it is received. In some cases, the intake system110 can utilize one or more rules to process the data. In certainembodiments, the intake system 110 can enrich the received data. Forexample, the intake system may add one or more fields to the datareceived from the host devices 104, such as fields denoting the host,source, sourcetype, index, or tenant associated with the incoming data.In certain embodiments, the intake system 110 can perform additionalprocessing on the incoming data, such as transforming structured datainto unstructured data (or vice versa), identifying timestampsassociated with the data, removing extraneous data, parsing data,indexing data, separating data, categorizing data, routing data based oncriteria relating to the data being routed, and/or performing other datatransformations, etc.

In some cases, the data processed by the intake system 110 can becommunicated or made available to the indexing system 112, the querysystem 114, and/or to other systems. In some embodiments, the intakesystem 110 communicates or makes available streams of data using one ormore shards. For example, the indexing system 112 may read or receivedata from one shard and another system may receive data from anothershard. As another example, multiple systems may receive data from thesame shard.

As used herein, a partition can refer to a logical division of data. Insome cases, the logical division of data may refer to a portion of adata stream, such as a shard from the intake system 110. In certaincases, the logical division of data can refer to an index or otherportion of data stored in the storage system 116, such as differentdirectories or file structures used to store data or buckets.Accordingly, it will be understood that the logical division of datareferenced by the term partition will be understood based on the contextof its use.

FIGS. 3B and 3C are block diagrams illustrating embodiments of variousdata structures for storing data processed by the system 102. FIG. 3Bincludes an expanded view illustrating an example of machine data storedin a data store 310 of the data storage system 116. It will beunderstood that the depiction of machine data and associated metadata asrows and columns in the table 319 of FIG. 3B is merely illustrative andis not intended to limit the data format in which the machine data andmetadata is stored in various embodiments described herein. In oneparticular embodiment, machine data can be stored in a compressed orencrypted format. In such embodiments, the machine data can be storedwith or be associated with data that describes the compression orencryption scheme with which the machine data is stored. The informationabout the compression or encryption scheme can be used to decompress ordecrypt the machine data, and any metadata with which it is stored, atsearch time.

In the illustrated embodiment of FIG. 3B, the data store 310 includes adirectory 312 (individually referred to as 312A, 312B) for each index(or partition) that contains a portion of data stored in the data store310 and a sub-directory 314 (individually referred to as 314A, 314B,314C) for one or more buckets of the index. In the illustratedembodiment of FIG. 3B, each sub-directory 314 corresponds to a bucketand includes an event data file 316 (individually referred to as 316A,316B, 316C) and an inverted index 318 (individually referred to as 318A,318B, 318C). However, it will be understood that each bucket can beassociated with fewer or more files and each sub-directory 314 can storefewer or more files.

In the illustrated embodiment, the data store 310 includes a _maindirectory 312A associated with an index “_main” and a _test directory312B associated with an index “_test.” However, the data store 310 caninclude fewer or more directories. In some embodiments, multiple indexescan share a single directory or all indexes can share a commondirectory. Additionally, although illustrated as a single data store310, it will be understood that the data store 310 can be implemented asmultiple data stores storing different portions of the information shownin FIG. 3C. For example, a single index can span multiple directories ormultiple data stores.

Furthermore, although not illustrated in FIG. 3B, it will be understoodthat, in some embodiments, the data store 310 can include directoriesfor each tenant and sub-directories for each index of each tenant, orvice versa. Accordingly, the directories 312A and 312B can, in certainembodiments, correspond to sub-directories of a tenant or includesub-directories for different tenants.

In the illustrated embodiment of FIG. 3B, two sub-directories 314A, 314Bof the _main directory 312A and one sub-directory 312C of the testdirectory 312B are shown. The sub-directories 314A, 314B, 314C cancorrespond to buckets of the indexes associated with the directories312A, 312B. For example, the sub-directories 314A and 314B cancorrespond to buckets “B1” and “B2,” respectively, of the index “_main”and the sub-directory 314C can correspond to bucket “B1” of the index“_test.” Accordingly, even though there are two “B1” buckets shown, aseach “B1” bucket is associated with a different index (and correspondingdirectory 312), the system 102 can uniquely identify them.

Although illustrated as buckets “B1” and “B2,” it will be understoodthat the buckets (and/or corresponding sub-directories 314) can be namedin a variety of ways. In certain embodiments, the bucket (orsub-directory) names can include information about the bucket. Forexample, the bucket name can include the name of the index with whichthe bucket is associated, a time range of the bucket, etc.

As described herein, each bucket can have one or more files associatedwith it, including, but not limited to one or more raw machine datafiles, bucket summary files, filter files, inverted indexes (alsoreferred to herein as high performance indexes or keyword indexes),permissions files, configuration files, etc. In the illustratedembodiment of FIG. 3B, the files associated with a particular bucket canbe stored in the sub-directory corresponding to the particular bucket.Accordingly, the files stored in the sub-directory 314A can correspondto or be associated with bucket “B1,” of index “_main,” the files storedin the sub-directory 314B can correspond to or be associated with bucket“B2” of index “_main,” and the files stored in the sub-directory 314Ccan correspond to or be associated with bucket “B1” of index “_test.”

FIG. 3B further illustrates an expanded event data file 316C showing anexample of data that can be stored therein. In the illustratedembodiment, four events 320, 322, 324, 326 of the machine data file 316Care shown in four rows. Each event 320-326 includes machine data 330 anda timestamp 332. The machine data 330 can correspond to the machine datareceived by the system 102. For example, in the illustrated embodiment,the machine data 330 of events 320, 322, 324, 326 corresponds toportions 302A, 302B, 302C, 302D, respectively, of the machine data 302after it was processed by the indexing system 112.

Metadata 334-338 associated with the events 320-326 is also shown in thetable 319. In the illustrated embodiment, the metadata 334-338 includesinformation about a host 334, source 336, and sourcetype 338 associatedwith the events 320-326. Any of the metadata can be extracted from thecorresponding machine data, or supplied or defined by an entity, such asa user or computer system. The metadata fields 334-338 can become partof, stored with, or otherwise associated with the events 320-326. Incertain embodiments, the metadata 334-338 can be stored in a separatefile of the sub-directory 314C and associated with the machine data file316C. In some cases, while the timestamp 332 can be extracted from theraw data of each event, the values for the other metadata fields may bedetermined by the indexing system 112 based on information it receivespertaining to the host device 104 or data source of the data separatefrom the machine data.

While certain default or user-defined metadata fields can be extractedfrom the machine data for indexing purposes, the machine data within anevent can be maintained in its original condition. As such, inembodiments in which the portion of machine data included in an event isunprocessed or otherwise unaltered, it is referred to herein as aportion of raw machine data. For example, in the illustrated embodiment,the machine data of events 320-326 is identical to the portions of themachine data 302A-302D, respectively, used to generate a particularevent. Similarly, the entirety of the machine data 302 may be foundacross multiple events. As such, unless certain information needs to beremoved for some reasons (e.g. extraneous information, confidentialinformation), all the raw machine data contained in an event can bepreserved and saved in its original form. Accordingly, the data store inwhich the event records are stored is sometimes referred to as a “rawrecord data store.” The raw record data store contains a record of theraw event data tagged with the various fields.

In other embodiments, the portion of machine data in an event can beprocessed or otherwise altered relative to the machine data used tocreate the event. With reference to the machine data 304, the machinedata of a corresponding event (or events) may be modified such that onlya portion of the machine data 304 is stored as one or more events. Forexample, in some cases, only machine data 304B of the machine data 304may be retained as one or more events or the machine data 304 may bealtered to remove duplicate data, confidential information, etc.

In FIG. 3B, the first three rows of the table 319 present events 320,322, and 324 and are related to a server access log that recordsrequests from multiple clients processed by a server, as indicated byentry of “access.log” in the source column 336. In the example shown inFIG. 3B, each of the events 320-324 is associated with a discreterequest made to the server by a client. The raw machine data generatedby the server and extracted from a server access log can include the IPaddress 340 of the client, the user id 341 of the person requesting thedocument, the time 342 the server finished processing the request, therequest line 343 from the client, the status code 344 returned by theserver to the client, the size of the object 345 returned to the client(in this case, the gif file requested by the client) and the time spent346 to serve the request in microseconds. In the illustrated embodimentsof FIGS. 3A, 3B, all the raw machine data retrieved from the serveraccess log is retained and stored as part of the corresponding events320-324 in the file 316C.

Event 326 is associated with an entry in a server error log, asindicated by “error.log” in the source column 336 that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 326 can be preserved and storedas part of the event 326.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 3B is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various embodiments of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user can continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.

FIG. 3C illustrates an embodiment of another file that can be includedin one or more subdirectories 314 or buckets. Specifically, FIG. 3Cillustrates an exploded view of an embodiments of an inverted index 318Bin the sub-directory 314B, associated with bucket “B2” of the index“_main,” as well as an event reference array 340 associated with theinverted index 318B.

In some embodiments, the inverted indexes 318 can correspond to distincttime-series buckets. As such, each inverted index 318 can correspond toa particular range of time for an index. In the illustrated embodimentof FIG. 3C, the inverted indexes 318A, 318B correspond to the buckets“B1” and “B2,” respectively, of the index “_main,” and the invertedindex 318C corresponds to the bucket “B1” of the index “test.” In someembodiments, an inverted index 318 can correspond to multipletime-series buckets (e.g., include information related to multiplebuckets) or inverted indexes 318 can correspond to a single time-seriesbucket.

Each inverted index 318 can include one or more entries, such as keyword(or token) entries 352 or field-value pair entries 354. Furthermore, incertain embodiments, the inverted indexes 318 can include additionalinformation, such as a time range 356 associated with the inverted indexor an index identifier 358 identifying the index associated with theinverted index 318. It will be understood that each inverted index 318can include less or more information than depicted. For example, in somecases, the inverted indexes 318 may omit a time range 356 and/or indexidentifier 358. In some such embodiments, the index associated with theinverted index 318 can be determined based on the location (e.g.,directory 312) of the inverted index 318 and/or the time range of theinverted index 318 can be determined based on the name of thesub-directory 314.

Token entries, such as token entries 352 illustrated in inverted index318B, can include a token 352A (e.g., “error,” “itemID,” etc.) and eventreferences 352B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated embodiment of FIG. 3C, the error tokenentry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding toevents located in the bucket “B2” of the index “_main.”

In some cases, some token entries can be default entries, automaticallydetermined entries, or user specified entries. In some embodiments, theindexing system 112 can identify each word or string in an event as adistinct token and generate a token entry for the identified word orstring. In some cases, the indexing system 112 can identify thebeginning and ending of tokens based on punctuation, spaces, etc. Incertain cases, the indexing system 112 can rely on user input or aconfiguration file to identify tokens for token entries 352, etc. Itwill be understood that any combination of token entries can be includedas a default, automatically determined, or included based onuser-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries354 shown in inverted index 318B, can include a field-value pair 354Aand event references 354B indicative of events that include a fieldvalue that corresponds to the field-value pair (or the field-valuepair). For example, for a field-value pair sourcetype::sendmail, afield-value pair entry 354 can include the field-value pair“sourcetype::sendmail” and a unique identifier, or event reference, foreach event stored in the corresponding time-series bucket that includesa sourcetype “sendmail.”

In some cases, the field-value pair entries 354 can be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields“host,” “source,” and “sourcetype” can be included in the invertedindexes 318 as a default. As such, all of the inverted indexes 318 caninclude field-value pair entries for the fields “host,” “source,” and“sourcetype.” As yet another non-limiting example, the field-value pairentries for the field “IP_address” can be user specified and may onlyappear in the inverted index 318B or the inverted indexes 318A, 318B ofthe index “_main” based on user-specified criteria. As anothernon-limiting example, as the indexing system 112 indexes the events, itcan automatically identify field-value pairs and create field-value pairentries 354. For example, based on the indexing system's 212 review ofevents, it can identify IP_address as a field in each event and add theIP_address field-value pair entries to the inverted index 318B (e.g.,based on punctuation, like two keywords separated by an ‘=’ or ‘:’etc.). It will be understood that any combination of field-value pairentries can be included as a default, automatically determined, orincluded based on user-specified criteria.

With reference to the event reference array 350, each unique identifier360, or event reference, can correspond to a unique event located in thetime series bucket or machine data file 316B. The same event referencecan be located in multiple entries of an inverted index 318. For exampleif an event has a sourcetype “splunkd,” host “www1” and token “warning,”then the unique identifier for the event can appear in the field-valuepair entries 344 “sourcetype::splunkd” and “host::www1,” as well as thetoken entry “warning.” With reference to the illustrated embodiment ofFIG. 3C and the event that corresponds to the event reference 3, theevent reference 3 is found in the field-value pair entries 344“host::hostA,” “source::sourceB,” “sourcetype::sourcetypeA,” and“IP_address::91.205.189.15” indicating that the event corresponding tothe event references is from hostA, sourceB, of sourcetypeA, andincludes “91.205.189.15” in the event data.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex 318 may include four sourcetype field-value pair entries 344corresponding to four different sourcetypes of the events stored in abucket (e.g., sourcetypes: sendmail, splunkd, web_access, andweb_service). Within those four sourcetype field-value pair entries, anidentifier for a particular event may appear in only one of thefield-value pair entries. With continued reference to the exampleillustrated embodiment of FIG. 3C, since the event reference 7 appearsin the field-value pair entry “sourcetype::sourcetypeA,” then it doesnot appear in the other field-value pair entries for the sourcetypefield, including “sourcetype::sourcetypeB,” “sourcetype::sourcetypeC,”and “sourcetype::sourcetypeD.”

The event references 360 can be used to locate the events in thecorresponding bucket or machine data file 316. For example, the invertedindex 318B can include, or be associated with, an event reference array350. The event reference array 350 can include an array entry 360 foreach event reference in the inverted index 318B. Each array entry 360can include location information 362 of the event corresponding to theunique identifier (non-limiting example: seek address of the event,physical address, slice ID, etc.), a timestamp 364 associated with theevent, or additional information regarding the event associated with theevent reference, etc.

For each token entry 352 or field-value pair entry 354, the eventreference 352B, 354B, respectively, or unique identifiers can be listedin chronological order or the value of the event reference can beassigned based on chronological data, such as a timestamp associatedwith the event referenced by the event reference. For example, the eventreference 1 in the illustrated embodiment of FIG. 3C can correspond tothe first-in-time event for the bucket, and the event reference 12 cancorrespond to the last-in-time event for the bucket. However, the eventreferences can be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order (e.g.,based on time received or added to the machine data file), etc. Further,the entries can be sorted. For example, the entries can be sortedalphabetically (collectively or within a particular group), by entryorigin (e.g., default, automatically generated, user-specified, etc.),by entry type (e.g., field-value pair entry, token entry, etc.), orchronologically by when added to the inverted index, etc. In theillustrated embodiment of FIG. 3C, the entries are sorted first by entrytype and then alphabetically.

In some cases, inverted indexes 318 can decrease the search time of aquery. For example, for a statistical query, by using the invertedindex, the system 102 can avoid the computational overhead of parsingindividual events in a machine data file 316. Instead, the system 102can use the inverted index 318 separate from the raw record data storeto generate responses to the received queries.

3.0. Query Processing and Execution

FIG. 4A is a flow diagram illustrating an embodiment of a routineimplemented by the query system 114 for executing a query. At block 402,the query system 114 receives a search query. As described herein, thequery can be in the form of a pipelined command language or other querylanguage and include filter criteria used to identify a set of data andprocessing criteria used to process the set of data.

At block 404, the query system 114 processes the query. As part ofprocessing the query, the query system 114 can determine whether thequery was submitted by an authenticated user and/or review the query todetermine that it is in a proper format for the data intake and querysystem 102, has correct semantics and syntax, etc. In addition, thequery system 114 can determine what, if any, configuration files orother configurations to use as part of the query.

In addition as part of processing the query, the query system 114 candetermine what portion(s) of the query to execute in a distributedmanner (e.g., what to delegate to search nodes) and what portions of thequery to execute in a non-distributed manner (e.g., what to execute onthe search head). For the parts of the query that are to be executed ina distributed manner, the query system 114 can generate specificcommands, for the components that are to execute the query. This mayinclude generating subqueries, partial queries or different phases ofthe query for execution by different components of the query system 114.In some cases, the query system 114 can use map-reduce techniques todetermine how to map the data for the search and then reduce the data.Based on the map-reduce phases, the query system 114 can generate querycommands for different components of the query system 114.

As part of processing the query, the query system 114 can determinewhere to obtain the data. For example, in some cases, the data mayreside on one or more indexing nodes or search nodes, as part of thestorage system 116 or may reside in a shared storage system or a systemexternal to the system 102. In some cases, the query system 114 candetermine what components to use to obtain and process the data. Forexample, the query system 114 can identify search nodes that areavailable for the query, etc.

At block 406, the query system 1206 distributes the determined portionsor phases of the query to the appropriate components (e.g., searchnodes). In some cases, the query system 1206 can use a catalog todetermine which components to use to execute the query (e.g., whichcomponents include relevant data and/or are available, etc.).

At block 408, the components assigned to execute the query, execute thequery. As mentioned, different components may execute different portionsof the query. In some cases, multiple components (e.g., multiple searchnodes) may execute respective portions of the query concurrently andcommunicate results of their portion of the query to another component(e.g., search head). As part of the identifying the set of data orapplying the filter criteria, the components of the query system 114 cansearch for events that match the criteria specified in the query. Thesecriteria can include matching keywords or specific values for certainfields. The searching operations at block 408 may use the late-bindingschema to extract values for specified fields from events at the timethe query is processed. In some embodiments, one or more rules forextracting field values may be specified as part of a sourcetypedefinition in a configuration file or in the query itself. In certainembodiments where search nodes are used to obtain the set of data, thesearch nodes can send the relevant events back to the search head, oruse the events to determine a partial result, and send the partialresult back to the search head.

At block 410, the query system 114 combines the partial results and/orevents to produce a final result for the query. As mentioned, in somecases, combining the partial results and/or finalizing the results caninclude further processing the data according to the query. Suchprocessing may entail joining different set of data, transforming thedata, and/or performing one or more mathematical operations on the data,preparing the results for display, etc.

In some examples, the results of the query are indicative of performanceor security of the IT environment and may help improve the performanceof components in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results can include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result can include one ormore calculated values derived from the matching events.

The results generated by the query system 114 can be returned to aclient using different techniques. For example, one technique streamsresults or relevant events back to a client in real-time as they areidentified. Another technique waits to report the results to the clientuntil a complete set of results (which may include a set of relevantevents or a result based on relevant events) is ready to return to theclient. Yet another technique streams interim results or relevant eventsback to the client in real-time until a complete set of results isready, and then returns the complete set of results to the client. Inanother technique, certain results are stored as “search jobs” and theclient may retrieve the results by referring to the search jobs.

The query system 114 can also perform various operations to make thesearch more efficient. For example, before the query system 114 beginsexecution of a query, it can determine a time range for the query and aset of common keywords that all matching events include. The querysystem 114 may then use these parameters to obtain a superset of theeventual results. Then, during a filtering stage, the query system 114can perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.In some cases, to make the search more efficient, the query system 114can use information known about certain data sets that are part of thequery to filter other data sets. For example, if an early part of thequery includes instructions to obtain data with a particular field, butlater commands of the query do not rely on the data with that particularfield, the query system 114 can omit the superfluous part of the queryfrom execution.

Various embodiments of the present disclosure can be implemented using,or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands can include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query can thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, can be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence can include filter criteria usedto search or filter for specific data. The results of the first commandcan then be passed to another command listed later in the commandsequence for further processing.

In various embodiments, a query can be formulated as a command sequencedefined in a command line of a search UI. In some embodiments, a querycan be formulated as a sequence of SPL commands. Some or all of the SPLcommands in the sequence of SPL commands can be separated from oneanother by a pipe symbol “|.” In such embodiments, a set of data, suchas a set of events, can be operated on by a first SPL command in thesequence, and then a subsequent SPL command following a pipe symbol “|”after the first SPL command operates on the results produced by thefirst SPL command or other set of data, and so on for any additional SPLcommands in the sequence. As such, a query formulated using SPLcomprises a series of consecutive commands that are delimited by pipe“|” characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for one of the subsequent commands (to the right of thepipe). This enables formulation of queries defined by a pipeline ofsequenced commands that refines or enhances the data at each step alongthe pipeline until the desired results are attained. Accordingly,various embodiments described herein can be implemented with SplunkProcessing Language (SPL) used in conjunction with the SPLUNK®ENTERPRISE system.

While a query can be formulated in many ways, a query can start with asearch command and one or more corresponding search terms or filtercriteria at the beginning of the pipeline. Such search terms or filtercriteria can include any combination of keywords, phrases, times, dates,Boolean expressions, fieldname-field value pairs, etc. that specifywhich results should be obtained from different locations. The resultscan then be passed as inputs into subsequent commands in a sequence ofcommands by using, for example, a pipe character. The subsequentcommands in a sequence can include directives for additional processingof the results once it has been obtained from one or more indexes. Forexample, commands may be used to filter unwanted information out of theresults, extract more information, evaluate field values, calculatestatistics, reorder the results, create an alert, create summary of theresults, or perform some type of aggregation function. In someembodiments, the summary can include a graph, chart, metric, or othervisualization of the data. An aggregation function can include analysisor calculations to return an aggregate value, such as an average value,a sum, a maximum value, a root mean square, statistical values, and thelike.

Due to its flexible nature, use of a pipelined command language invarious embodiments is advantageous because it can perform “filtering”as well as “processing” functions. In other words, a single query caninclude a search command and search term expressions, as well asdata-analysis expressions. For example, a command at the beginning of aquery can perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step can then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g. calculation of an aggregate value related to thefiltered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand can allow events to be filtered by keyword as well as fieldcriteria. For example, a search command can filter events based on theword “warning” or filter events based on a field value “10.0.1.2”associated with a field “clientip.”

The results obtained or generated in response to a command in a querycan be considered a set of results data. The set of results data can bepassed from one command to another in any data format. In oneembodiment, the set of result data can be in the form of a dynamicallycreated table. Each command in a particular query can redefine the shapeof the table. In some implementations, an event retrieved from an indexin response to a query can be considered a row with a column for eachfield value. Columns can contain basic information about the data and/ordata that has been dynamically extracted at search time.

FIG. 4B provides a visual representation of the manner in which apipelined command language or query can operate in accordance with thedisclosed embodiments. The query 430 can be input by the user andsubmitted to the query system 114. In the illustrated embodiment, thequery 430 comprises filter criteria 430A, followed by two commands 430B,430C (namely, Command1 and Command2). Disk 422 represents data as it isstored in a data store to be searched. For example, disk 422 canrepresent a portion of the storage system 116 or some other data storethat can be searched by the query system 114. Individual rows of canrepresent different events and columns can represent different fieldsfor the different events. In some cases, these fields can include rawmachine data, host, source, and sourcetype.

At block 440, the query system 114 uses the filter criteria 430A (e.g.,“sourcetype=syslog ERROR”) to filter events stored on the disk 422 togenerate an intermediate results table 424. Given the semantics of thequery 430 and order of the commands, the query system 114 can executethe filter criteria 430A portion of the query 430 before executingCommand1 or Command2.

Rows in the table 424 may represent individual records, where eachrecord corresponds to an event in the disk 422 that satisfied the filtercriteria. Columns in the table 424 may correspond to different fields ofan event or record, such as “user,” “count,” percentage,” “timestamp,”or the raw machine data of an event, etc. Notably, the fields in theintermediate results table 424 may differ from the fields of the eventson the disk 422. In some cases, this may be due to the late bindingschema described herein that can be used to extract field values atsearch time. Thus, some of the fields in table 424 may not have existedin the events on disk 422.

Illustratively, the intermediate results table 424 has fewer rows thanwhat is shown in the disk 422 because only a subset of events retrievedfrom the disk 422 matched the filter criteria 430A “sourcetype=syslogERROR.” In some embodiments, instead of searching individual events orraw machine data, the set of events in the intermediate results table424 may be generated by a call to a pre-existing inverted index.

At block 442, the query system 114 processes the events of the firstintermediate results table 424 to generate the second intermediateresults table 426. With reference to the query 430, the query system 114processes the events of the first intermediate results table 424 toidentify the top users according to Command1. This processing mayinclude determining a field value for the field “user” for each recordin the intermediate results table 424, counting the number of uniqueinstances of each “user” field value (e.g., number of users with thename David, John, Julie, etc.) within the intermediate results table424, ordering the results from largest to smallest based on the count,and then keeping only the top 10 results (e.g., keep an identificationof the top 10 most common users). Accordingly, each row of table 426 canrepresent a record that includes a unique field value for the field“user,” and each column can represent a field for that record, such asfields “user,” “count,” and “percentage.”

At block 444, the query system 114 processes the second intermediateresults table 426 to generate the final results table 428. Withreference to query 430, the query system 114 applies the command“fields—present” to the second intermediate results table 426 togenerate the final results table 428. As shown, the command“fields—present” of the query 430 results in one less column, which mayrepresent that a field was removed during processing. For example, thequery system 114 may have determined that the field “percentage” wasunnecessary for displaying the results based on the Command2. In such ascenario, each record of the final results table 428 would include afield “user,” and “count.” Further, the records in the table 428 wouldbe ordered from largest count to smallest count based on the querycommands.

It will be understood that the final results table 428 can be a thirdintermediate results table, which can be pipelined to another stagewhere further filtering or processing of the data can be performed,e.g., preparing the data for display purposes, filtering the data basedon a condition, performing a mathematical calculation with the data,etc. In different embodiments, other query languages, such as theStructured Query Language (“SQL”), can be used to create a query.

As described herein, extraction rules can be used to extract field-valuepairs or field values from data. An extraction rule can comprise one ormore regex rules that specify how to extract values for the fieldcorresponding to the extraction rule. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. Extraction rules can be used toextract one or more values for a field from events by parsing theportions of machine data in the events and examining the data for one ormore patterns of characters, numbers, delimiters, etc., that indicatewhere the field begins and, optionally, ends. In certain embodiments,extraction rules can be stored in one or more configuration files. Insome cases, a query itself can specify one or more extraction rules.

In some cases, extraction rules can be applied at data ingest by theintake system 110 and/or indexing system 112. For example, the intakesystem 110 and indexing system 112 can apply extraction rules toingested data and/or events generated from the ingested data and storeresults in an inverted index.

The system 102 advantageously allows for search time field extraction.In other words, fields can be extracted from the event data at searchtime using late-binding schema as opposed to at data ingestion time,which was a major limitation of the prior art systems. Accordingly,extraction rules can be applied at search time by the query system 114.The query system can apply extraction rules to events retrieved from thestorage system 116 or data received from sources external to the system102. Extraction rules can be applied to all the events in the storagesystem 116 or to a subset of the events that have been filtered based onsome filter criteria (e.g., event timestamp values, etc.).

FIG. 4C is a block diagram illustrating an embodiment of the table 319showing events 320-326, described previously with reference to FIG. 3B.As described herein, the table 319 is for illustrative purposes, and theevents 320-326 may be stored in a variety of formats in an event datafile 316 or raw record data store. Further, it will be understood thatthe event data file 316 or raw record data store can store millions ofevents. FIG. 4C also illustrates an embodiment of a search bar 450 forentering a query and a configuration file 452 that includes variousextraction rules that can be applied to the events 320-326.

As a non-limiting example, if a user inputs a query into search bar 450that includes only keywords (also known as “tokens”), e.g., the keyword“error” or “warning,” the query system 114 can search for those keywordsdirectly in the events 320-326 stored in the raw record data store.

As described herein, the indexing system 112 can optionally generate anduse an inverted index with keyword entries to facilitate fast keywordsearching for event data. If a user searches for a keyword that is notincluded in the inverted index, the query system 114 may nevertheless beable to retrieve the events by searching the event data for the keywordin the event data file 316 or raw record data store directly. Forexample, if a user searches for the keyword “eva,” and the name “eva”has not been indexed at search time, the query system 114 can search theevents 320-326 directly and return the first event 320. In the casewhere the keyword has been indexed, the inverted index can include areference pointer that will allow for a more efficient retrieval of theevent data from the data store. If the keyword has not been indexed, thequery system 114 can search through the events in the event data file toservice the search.

In many cases, a query include fields. The term “field” refers to alocation in the event data containing one or more values for a specificdata item. Often, a field is a value with a fixed, delimited position ona line, or a name and value pair, where there is a single value to eachfield name. A field can also be multivalued, that is, it can appear morethan once in an event and have a different value for each appearance,e.g., email address fields. Fields are searchable by the field name orfield name-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the query, “status=404.” This searchquery finds events with “status” fields that have a value of “404.” Whenthe search is run, the query system 114 does not look for events withany other “status” value. It also does not look for events containingother fields that share “404” as a value. As a result, the searchreturns a set of results that are more focused than if “404” had beenused in the search string as part of a keyword search. Note also thatfields can appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string: “Nov15 09:33:22 evaemerson.”

FIG. 4C illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a query, the querysystem 114 determines if the query references a “field.” For example, aquery may request a list of events where the “clientip” field equals“127.0.0.1.” If the query itself does not specify an extraction rule andif the field is not an indexed metadata field, e.g., time, host, source,sourcetype, etc., then in order to determine an extraction rule, thequery system 114 may, in one or more embodiments, locate configurationfile 452 during the execution of the query.

Configuration file 452 may contain extraction rules for various fields,e.g., the “clientip” field. The extraction rules may be inserted intothe configuration file 452 in a variety of ways. In some embodiments,the extraction rules can comprise regular expression rules that aremanually entered in by the user.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system can then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 452.

In some embodiments, the indexing system 112 can automatically discovercertain custom fields at index time and the regular expressions forthose fields will be automatically generated at index time and stored aspart of extraction rules in configuration file 452. For example, fieldsthat appear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

Events from heterogeneous sources that are stored in the storage system116 may contain the same fields in different locations due todiscrepancies in the format of the data generated by the varioussources. For example, event 326 also contains a “clientip” field,however, the “clientip” field is in a different format from events 320,322, and 324. Furthermore, certain events may not contain a particularfield at all. To address the discrepancies in the format and content ofthe different types of events, the configuration file 452 can specifythe set of events to which an extraction rule applies. For example,extraction rule 454 specifies that it is to be used with events having asourcetype “access_combined,” and extraction rule 456 specifies that itis to be used with events having a sourcetype “apache_error.” Otherextraction rules shown in configuration file 452 specify a set or typeof events to which they apply. In addition, the extraction rules shownin configuration file 452 include a regular expression for parsing theidentified set of events to determine the corresponding field value.Accordingly, each extraction rule may pertain to only a particular typeof event. Accordingly, if a particular field, e.g., “clientip” occurs inmultiple types of events, each of those types of events can have its owncorresponding extraction rule in the configuration file 452 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. In some cases, the sets of eventsare grouped by sourcetype because events generated by a particularsource can have the same format.

The field extraction rules stored in configuration file 452 can be usedto perform search-time field extractions. For example, for a query thatrequests a list of events with sourcetype “access_combined” where the“clientip” field equals “127.0.0.1,” the query system 114 can locate theconfiguration file 452 to retrieve extraction rule 454 that allows it toextract values associated with the “clientip” field from the eventswhere the sourcetype is “access_combined” (e.g., events 320-324). Afterthe “clientip” field has been extracted from the events 320, 322, 324,the query system 114 can then apply the field criteria by performing acompare operation to filter out events where the “clientip” field doesnot equal “127.0.0.1.” In the example shown in FIG. 4C, the events 320and 322 would be returned in response to the user query. In this manner,the query system 114 can service queries with filter criteria containingfield criteria and/or keyword criteria.

It should also be noted that any events filtered by performing asearch-time field extraction using a configuration file 452 can befurther processed by directing the results of the filtering step to aprocessing step using a pipelined search language. Using the priorexample, a user can pipeline the results of the compare step to anaggregate function by asking the query system 114 to count the number ofevents where the “clientip” field equals “127.0.0.1.”

By providing the field definitions for the queried fields at searchtime, the configuration file 452 allows the event data file or rawrecord data store to be field searchable. In other words, the raw recorddata store can be searched using keywords as well as fields, wherein thefields are searchable name/value pairings that can distinguish one eventfrom another event and can be defined in configuration file 452 usingextraction rules. In comparison to a search containing field names, akeyword search may result in a search of the event data directly withoutthe use of a configuration file.

Further, the ability to add schema to the configuration file 452 atsearch time results in increased efficiency and flexibility. A user cancreate new fields at search time and simply add field definitions to theconfiguration file 452. As a user learns more about the data in theevents, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules in the configuration file for use the next time the schema is usedby the system 102. Because the system 102 maintains the underlying rawdata and uses late-binding schema for searching the raw data, it enablesa user to continue investigating and learn valuable insights about theraw data long after data ingestion time. Similarly, multiple fielddefinitions can be added to the configuration file to capture the samefield across events generated by different sources or sourcetypes. Thisallows the system 102 to search and correlate data across heterogeneoussources flexibly and efficiently.

The system 102 can use one or more data models to search and/or betterunderstand data. A data model is a hierarchically structured search-timemapping of semantic knowledge about one or more datasets. It encodes thedomain knowledge used to build a variety of specialized searches ofthose datasets. Those searches, in turn, can be used to generatereports.

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

Performing extraction and analysis operations at search time can involvea large amount of data and require a large number of computationaloperations, which can cause delays in processing the queries. In someembodiments, the system 102 can employ a number of unique accelerationtechniques to speed up analysis operations performed at search time.These techniques include: performing search operations in parallel usingmultiple components of the query system 114, using an inverted index118, and accelerating the process of generating reports.

To facilitate faster query processing, a query can be structured suchthat multiple components of the query system 114 (e.g., search nodes)perform the query in parallel, while aggregation of search results fromthe multiple components is performed at a particular component (e.g.,search head). For example, consider a scenario in which a user entersthe query “Search “error”|stats count BY host.” The query system 114 canidentify two phases for the query, including: (1) subtasks (e.g., dataretrieval or simple filtering) that may be performed in parallel bymultiple components, such as search nodes, and (2) a search resultsaggregation operation to be executed by one component, such as thesearch head, when the results are ultimately collected from the searchnodes.

Based on this determination, the query system 114 can generate commandsto be executed in parallel by the search nodes, with each search nodeapplying the generated commands to a subset of the data to be searched.In this example, the query system 114 generates and then distributes thefollowing commands to the individual search nodes: “Search “error”prestats count BY host.” In this example, the “prestats” command canindicate that individual search nodes are processing a subset of thedata and are responsible for producing partial results and sending themto the search head. After the search nodes return the results to thesearch head, the search head aggregates the received results to form asingle search result set. By executing the query in this manner, thesystem effectively distributes the computational operations across thesearch nodes while reducing data transfers. It will be understood thatthe query system 114 can employ a variety of techniques to usedistributed components to execute a query. In some embodiments, thequery system 114 can use distributed components for only mappingfunctions of a query (e.g., gather data, applying filter criteria,etc.). In certain embodiments, the query system 114 can use distributedcomponents for mapping and reducing functions (e.g., joining data,combining data, reducing data, etc.) of a query.

4.0. Example Use Cases

The system 102 provides various schemas, dashboards, and visualizationsthat simplify developers' tasks to create applications with additionalcapabilities, including but not limited to security, data centermonitoring, IT service monitoring, and client/customer insights.

An embodiment of an enterprise security application is as SPLUNK®ENTERPRISE SECURITY, which performs monitoring and alerting operationsand includes analytics to facilitate identifying both known and unknownsecurity threats based on large volumes of data stored by the system102. The enterprise security application provides the securitypractitioner with visibility into security-relevant threats found in theenterprise infrastructure by capturing, monitoring, and reporting ondata from enterprise security devices, systems, and applications.Through the use of the system 102 searching and reporting capabilities,the enterprise security application provides a top-down and bottom-upview of an organization's security posture.

An embodiment of an IT monitoring application is SPLUNK® IT SERVICEINTELLIGENCE™, which performs monitoring and alerting operations. The ITmonitoring application also includes analytics to help an analystdiagnose the root cause of performance problems based on large volumesof data stored by the system 102 as correlated to the various servicesan IT organization provides (a service-centric view). This differssignificantly from conventional IT monitoring systems that lack theinfrastructure to effectively store and analyze large volumes ofservice-related events. Traditional service monitoring systems typicallyuse fixed schemas to extract data from pre-defined fields at dataingestion time, wherein the extracted data is typically stored in arelational database. This data extraction process and associatedreduction in data content that occurs at data ingestion time inevitablyhampers future investigations, when all of the original data may beneeded to determine the root cause of or contributing factors to aservice issue.

In contrast, an IT monitoring application system stores large volumes ofminimally-processed service-related data at ingestion time for laterretrieval and analysis at search time, to perform regular monitoring, orto investigate a service issue. To facilitate this data retrievalprocess, the IT monitoring application enables a user to define an IToperations infrastructure from the perspective of the services itprovides. In this service-centric approach, a service such as corporatee-mail may be defined in terms of the entities employed to provide theservice, such as host machines and network devices. Each entity isdefined to include information for identifying all of the events thatpertains to the entity, whether produced by the entity itself or byanother machine, and considering the many various ways the entity may beidentified in machine data (such as by a URL, an IP address, or machinename). The service and entity definitions can organize events around aservice so that all of the events pertaining to that service can beeasily identified. This capability provides a foundation for theimplementation of Key Performance Indicators.

As described herein, the system 102 can receive heterogeneous data fromdisparate systems. In some cases, the data from the disparate systemsmay be related and correlating the data can result in insights intoclient or customer interactions with various systems of a vendor. To aidin the correlation of data across different systems, multiple fielddefinitions can be added to one or more configuration files to capturethe same field or data across events generated by different sources orsourcetypes. This can enable the system 102 to search and correlate dataacross heterogeneous sources flexibly and efficiently.

As a non-limiting example and with reference to FIG. 4D, consider ascenario in which a common customer identifier is found among log datareceived from three disparate data sources. In this example, a usersubmits an order for merchandise using a vendor's shopping applicationprogram 460 running on the user's system. In this example, the order wasnot delivered to the vendor's server due to a resource exception at thedestination server that is detected by the middleware code 462. The userthen sends a message to the customer support server 464 to complainabout the order failing to complete. The three systems 460, 462, 464 aredisparate systems that do not have a common logging format. The shoppingapplication program 460 sends log data 466 to the system 102 in oneformat, the middleware code 462 sends error log data 468 in a secondformat, and the support server 464 sends log data 470 in a third format.

Using the log data received at the system 102 from the three systems460, 462, 464, the vendor can uniquely obtain an insight into useractivity, user experience, and system behavior. The system 102 allowsthe vendor's administrator to search the log data from the three systems460, 462, 464, thereby obtaining correlated information, such as theorder number and corresponding customer ID number of the person placingthe order. The system 102 also allows the administrator to see avisualization of related events via a user interface. The administratorcan query the system 102 for customer ID field value matches across thelog data from the three systems 460, 462, 464 that are stored in thestorage system 116. While the customer ID field value exists in the datagathered from the three systems 460, 462, 464, it may be located indifferent areas of the data given differences in the architecture of thesystems. The query system 114 obtains events from the storage system 116related to the three systems 460, 462, 464. The query system 114 thenapplies extraction rules to the events in order to extract field valuesfor the field “customer ID” that it can correlate. As described herein,the query system 114 may apply a different extraction rule to each setof events from each system when the event format differs among systems.In this example, a user interface can display to the administrator theevents corresponding to the common customer ID field values 472, 474,and 476, thereby providing the administrator with insight into acustomer's experience. The system 102 can provide additional userinterfaces and reports to aid a user in analyzing the data associatedwith the customer.

5.0. Architecture Specific Description

FIG. 5 is an overview diagram of a computing system implementing one ormore of the disclosed embodiments. FIG. 5 shows a product managementsystem 502 in communication with two installations of a softwareprogram, a first installation of a software program 504 and a secondinstallation of a software program 506. The product management system502 trains a model based on input from each of the first installation ofa software program 504 and second installation of a software program506. For example, each of the first installation of a software program504 and the second installation of a software program 506 provide usageinformation, resource utilization information, and installed productinformation to the product management system 502. This suppliedinformation is shown as message 510 and message 512, transmitted by thefirst installation of a software program 504 and the second installationof a software program 506, respectively.

The product management system 502 also receives customer relationalinformation from a customer relationship management (CRM) system 508,via a CRM info message 514. The CRM system 508 provides informationpertaining to a plurality of customers. Information such as recentpurchases, previous purchases, and firmographics (e.g. data regardingthe customer, such as data analogous to demographic information of auser). Firmographics includes, in various embodiments, one or more of anumber of employees at the customer, the customer's line ofbusiness/industry, terms of the customer's license, or otherinformation. In some embodiments, the product management system 502trains the model based on the CRM information received from the CRMsystem 508, as discussed below.

The trained model is provided by the product management system 502 toeach of the first installation of a software program 504 and the secondinstallation of a software program 506 via a first deployment message516 and a second deployment message 518. The trained model maps usageinformation determined locally at each of the first installation of asoftware program 504 and the second installation of a software program506 to one of a plurality of predefined states. Each of the predefinedstates define values for a plurality of configuration parameters. Eachof the first installation of a software program 504 and the secondinstallation of a software program 506 determine, from the model, whichof the predefined states is appropriate for their current operatingenvironment. Based on the predefined state most appropriate for theiroperating environment, each of the first installation of a softwareprogram 504 and the second installation of a software program 506updates their operational parameters defined by the predefined state.Thus, consistent with the description of FIG. 5 , some of the disclosedembodiments combine a comprehensive characterization or state of aninstallation of a software program that includes both values ofoperational parameters of the installation, in addition to informationrelating to a configuration of the installation, so as to providerecommendations or automated transitions of the installation from afirst operational state to a second operations state. The transitionfrom one state to another initiates changes in one or more configurationor operational parameters of the installation. Such an approachfacilitates scale and generality.

FIG. 6 is a state transition diagram 600 illustrating example statetransitions implemented in one or more of the disclosed embodiments.FIG. 6 shows three states, a first state 602, a second state 604, and athird state 606. Each state is defined by a plurality of parametervalues, such as parameter values 608. In the example of FIG. 6 , theparameter values 608 include a web access log volume 610, a number ofdaily active users 612, CPU utilization 614, a list of installedapplications 616, and a number of search heads parameter 618.

FIG. 6 shows that, while an example installation of a software programis operating in the first state 602, it receives a recommendation from amodel to install a “web analytics” application. This causes a statetransition 620 from the first state 602 to the second state 604. Due tothe state transition 620, the example installation of a software programimplementing the second state 604 initiates an installation of a “webanalytics” application, as defined by the second state 604. Thus, theparameter 621 shows that, as a result of operating in the second state604, the example installation of a software program installs the “webanalytics” application.

FIG. 6 also shows a state transition 622 from the second state 604 backto the first state 602. The state transition 622 is in response, in atleast some embodiments, to output from a machine learning modelindicating that the first state 602 is preferred or recommended for aninstallation of a software program given a current set of operationalparameter values. As a result, the example installation of a softwareprogram uninstalls the “web analytics” application, as reflected in thelist of installed applications 616.

FIG. 6 further illustrates a state transition 624 that indicates arecommendation to add a search node. As a result, the exampleinstallation of a software program transitions from the first state 602to the third state 606. As a result of operating in the third state 606,the example installation of a software program increases the number ofsearch heads from a number indicated by the number of search headsparameter 618 to a different number of search heads shown by theparameter 626.

FIG. 6 further shows a state transition 628 from the third state 606back to the first state 602 based on a recommendation from a model toreduce a number of search heads from the number specified by theparameter 626 to the number specified by the number of search headsparameter 618. Thus, the example installation of a software programoperates in the first state 602.

FIG. 7 shows an example machine learning module 700 according to someexamples of the present disclosure. Machine learning module 700 utilizesa training module 710 and a prediction module 720. Training module 710inputs historical information 730 into feature determination module 750a. The historical information 730 represents, in some embodiments, atraining database that stores training data for training a machinelearning model. In some embodiments, the historical information 730 islabeled. Example historical information includes operational parametervalues experienced by a plurality of different installations of thesoftware program. The historical operational parameters values areassociated, via the training data store, preferred or recommended statesof the installation of a software program. Labels included in thetraining library indicate which operational parameter values arerecommended or preferred given the historical operational parametervalues included in historical information 730.

Feature determination module 750 a determines one or more features 760from this historical information 730. Stated generally, features 760 area set of the information input and are determined to be predictive of aparticular outcome. In some examples, the features 760 may be all thehistorical information 730, but in other examples, the features 760 area subset of the historical information 730. The machine learningalgorithm 770 produces a model 718 based upon the features 760 and thelabels.

In the prediction module 720, current information 790 may be input tothe feature determination module 750 b. The current information 790 inthe disclosed embodiments include similar indications of that describedabove with respect to the historical information 730. For example, aninstallation of a computer program provides, in various embodiments, oneor more of the operational parameter values discussed above, thatcharacterize a current operating environment of the installation.

Feature determination module 750 b determines, in some embodiments, anequivalent set of features or a different set of features from thecurrent information 790 as feature determination module 750 a determinedfrom historical information 730. In some examples, feature determinationmodule 750 a and 750 b are the same module. Feature determination module750 b produces features 715, which is input into the model 718 togenerate a selection of a preferred or recommended operating state of aninstallation of a software program. The training module 710 may operatein an offline manner to train the model 718. For example, aninstallation of a software program, in some embodiments, trains a localmodel based on activity and/or configuration information present locally(e.g. with respect to the installation itself, without the benefit ofdata from other installations or the product management server). In someembodiments, a model provided by the product management server 502 isprovided to an installation of the software program, and then furthertrained locally by the installation.

The training module 710 also operates, in some embodiments, in an onlinemanner. For example, some embodiments of the training module 710 receivehistorical info 730 from one or more installations of a softwareprogram, and use this information to train the model 718.

The prediction module 720, generally operates locally at an installationof a software program. The installation invokes the model to determinewhether the installation should transition from an existing or firstoperating state to a second operating state, as discussed above. In someembodiments, the model 718 is made available via a web service or othertechnology that provides for remote invocation of the model. Forexample, in some embodiments, the product deployment server 502 executesthe model 718, and each of the installations of a software program (e.g.504 and/or 506) remotely provide input (e.g. current info 790) to themodel 718 executing at the product deployment server 502. As discussedabove, the model 718 may be periodically updated via additional trainingand/or user feedback.

The prediction module 720 generates one or more outputs 795. The outputsinclude, in some embodiments, a selection of a recommended or preferredoperating mode or state of an installation of a software product.

The machine learning algorithm 770 may be selected from among manydifferent potential supervised or unsupervised machine learningalgorithms. Examples of supervised learning algorithms includeartificial neural networks, Bayesian networks, instance-based learning,support vector machines, decision trees (e.g., Iterative Dichotomiser 3,C4.5, Classification and Regression Tree (CART), Chi-squared AutomaticInteraction Detector (CHAID), and the like), random forests, linearclassifiers, quadratic classifiers, k-nearest neighbor, linearregression, logistic regression, hidden Markov models, models based onartificial life, simulated annealing, and/or virology. Examples ofunsupervised learning algorithms include expectation-maximizationalgorithms, vector quantization, and information bottleneck method.Unsupervised models may not have a training module 710. In an exampleembodiment, a regression model is used and the model 718 is a vector ofcoefficients corresponding to a learned importance for each of thefeatures in the vector of features 760, 715. In some embodiments, tocalculate a score, a dot product of the features 715 and the vector ofcoefficients of the model 718 is taken.

FIG. 8 illustrates data flow during a model training process that isimplemented in one or more of the disclosed embodiments. FIG. 8 showsthe model 718 that was discussed above with respect to FIG. 7 . Themodel 718 is provided with training data that includes historicalinformation 802 and labels 804. The historical information includes CRMinformation, operating parameters, and operating state information for aplurality of customers. For example, FIG. 8 illustrates that the model718 is provided with CRM information 806 for a first customer and withCRM information 808 of a second customer. Each of the CRM information806 and CRM information 808 are provided, in some embodiments, withrespect to a plurality of different time periods (e.g. represented as t₀. . . t_(n) in FIG. 8 ). The CRM information includes, as discussedabove, in various embodiments, one or more of customer size, customerindustry indicator, time customer has been a customer, number ofemployees of the customer, license terms of the customer, recentpurchases by the customer, previous purchases by the customer,firmographics, or other information.

FIG. 8 also shows operating parameters 810 of a first customer andoperating parameters 812 of a second customer being provided to themodel 718. As discussed above with respect to the CRM information, insome embodiments, operating parameters are provided across a pluralityof predefined time periods to the model 718. As shown in FIG. 8 ,operating parameter values at each of times t₀ . . . t_(n) are providedfor each of the customer n and customer n+1.

Operating parameters of the customers are provided by one or moreinstallations of the software program of the customer. As discussedabove, operating parameters include, for example, one or more of anactive number of users, a total number of configured users, resourceutilization information such as CPU utilization, memory utilization,performance metrics such as throughput metrics (e.g. one or more metricsindicating a rate at which data is accessed or log files areread/written or otherwise accessed), latency metrics (e.g. such as adata access latency), or other performance metrics.

The historical information 802 also includes an existing or currentoperating state associated with the provided CRM info and operatingparameters of the historical information 802. As shown, the historicalinformation 802 includes an existing operating state 814 for a customer“n,” and existing operating state 816 for a customer “n+1.”

Along with the historical information 802, the CRM information andoperating parameters of the historical information 802 are labeled withlabels 804. The labels include a first operating state 818 of the firstcustomer and a second operating state 820 of the second customer. Thus,via the training illustrated in the training data flow 800, the model718 associates the CRM information 806, the operating parameters 810,and the existing or current operating state 814 with the first operatingstate 818. Note that in embodiments that provide historical informationfor a plurality of different time periods, the training data flow 800associates the multiple different historical information for thedifferent time periods with a single operating state (e.g. firstoperating state 818) for the respective customer. This is useful fortraining the model to recommend particular operating states based on ahistory of an installation of a software program, or a sequence ofhistorical evolutions of an operating state or operating parameters ofthe installation.

The training illustrated in FIG. 8 also allows the model 718 toassociate the CRM information 808, the operating parameters 812, and theexisting or current operating state 816 with the second operating state820. Thus, as a result of the training process described by FIG. 8 , themodel stores data that associates the inputs 802 with the labels 804.These associations then allow the model 718 to make “predictions”regarding a preferred or recommended operating state given a set ofoperational parameter values analogous to those of values included inthe historical information 802. Such a prediction process is describedbelow with respect to FIG. 9 .

FIG. 9 illustrates data flow during a model usage process that isimplemented in one or more of the disclosed embodiments. The data flow900 shows operating parameter values 910 of an installation of asoftware program being provided to the model 718. Based on the operatingparameter values 910, a customer identifier 912, and an existingoperating state of the customer 914, the model 718 generates arecommended or selected operating state indicator 920.

Thus, the data flow 900 illustrated with respect to FIG. 9 relies, in atleast some embodiments, on model training data developed during themodel training process of FIG. 8 . As discussed above, some embodimentsreceive a trained model, such as the model 718, from a productmanagement system (e.g. the product management system 502) at aninstallation of a software program (e.g. the first installation of asoftware program 504 and/or the second installation of a softwareprogram 506). In some embodiments, the installation of a softwareprogram then utilizes the received model to determine an operatingstate. As shown in FIG. 9 , the installation of a software programprovides operating parameter values 910 and a customer identifier 912 tothe model 718. Based on the provided operating parameter values 910, andthe customer identifier 912, the model 718 generates an operating stateindicator 920. The operating state indicator 920 indicates a suggestedoperating state for the installation of a software program to operatein, based on the installation of a software program's operatingparameter values 910. As discussed above, the operating state defines aplurality of parameters or sub-states that govern operation of theinstallation of a software program. These include, for example, a numberof search heads, a number of processing threads, a list of installedapplications, or other configuration information. The model 718 utilizesthe customer identifier 912 to identify an existing operating state ofthe customer, which can affect, which operating state is recommended orselected via the operating state indicator 920.

FIG. 10A illustrates data flow through a model in one or more of thedisclosed embodiments. The data flow 1000 shows the model 718 beingprovided with operational parameter values 910, a customer identifier912, and an indication of an existing operating state 914 of aninstallation of a software program at the customer. The operationalparameter values 910 both include read-only operational parameter values1010 and read/write operational parameter values 1020. The read-onlyoperational parameter values 1010 are those that cannot be directlymodified based on, for example, output from the model 718. Some examplesof read-only operational parameter values include resource utilizationmetrics, such as CPU utilization, memory utilization, or performancemetrics, such as latency or throughput metrics. Read/write operationalparameter values 1020 are those that can be modified directly by thedisclosed embodiments, for example, based on the operating stateindicator 920 output by the model 718. These include, for example, alist of applications installed as part of an installation of a softwareprogram, a number of search heads (e.g., an instance that distributessearches to other indexers, and in some embodiments, does not includeany indexes itself, or a reception point or process for incomingcomputational requests), a number of processing threads, or otherparameters. As discussed above, the model 718 outputs a selection of oneof a plurality of predefined states, with each state defining or beingassociated with values of one or more operational parameter values. Aninstallation of a software program relies on output from the model 718to set its operational parameter values according to those defined bythe state indicated by the model.

FIG. 10B illustrates a mapping from a plurality of predefined states1065 to configurations defining or being associated with operationalparameter values. The plurality of predefined states 1065 include afirst state 1070A, a second state 1070B, and a third state 1070C. Eachstate maps to a first configuration 1075A, a second configuration 1075B,or a third configuration 1075C, respectively. Each configuration definesa plurality of operational parameter values associated with theparticular configuration. For example, the first configuration 1075Adefines a first plurality of operational parameter values 1080A. Thesecond configuration 1075B defines a second plurality of operationalparameter values 1080B. The third configuration 1075C defines a thirdplurality of operational parameter values 1080C.

As discussed above, in some embodiments, a machine learning model istrained so as to select one of a plurality of predefined states, such asone of the states 1070A-C illustrated in FIG. 10B. In some embodiments,when an installation of a software program receives an indication of aselected state from the model, the installation of a software program isable to map from the selected state to a configuration, in a similarmanner to that illustrated in FIG. 10B (e.g., the first state 1070A mapsto the first configuration 1075A). The configuration defines a pluralityof operational parameter values, as also illustrated in FIG. 10B (e.g.the first configuration 1075A defines operational parameter values1080A).

Some examples of specific operational parameter values associated withone or more of the states 1070A-C include one or more of a number ofsearch heads utilized by the installation, a set of installedapplications included in the installation of the software program, anumber of computer processors executing one or more processes of theinstallation of the software program, a memory size of a computerallocated for execution of the installation of the software program, anumber of computing instances allocated for execution of theinstallation of the software program, a number of storage devices,utilized or allocated to the installation, a type of one or more storagedevices in use by the installation, a type of memory device used by theinstallation, a topology definition describing how computing instancesare interconnected and how they connection is configured, a level ofdata replication, an algorithm for data resiliency or fault recovery, adata compression algorithm, a list of user limits or capability (e.g.for a plurality of users), an application limit or capability, a numberof workload pools (e.g. units and aggregations around whichperformance-related resource allocations and configurations areapplied), a resource allocation to each workload pool, a data retentionpolicy of one or more computing instances of the installation, an amountof pre-processing of incoming data, a type of pre-processing of incomingdata, a definition of a schedule of automated computations performed bythe installation, a definition of automated generation of notificationsgenerated by the installation, a measurement of a number of concurrentcomputations, an order of computations, a level of fan-in and fan-outfor data flows (e.g. degrees of parallelism in aggregating results ofcomputations from different locations or subroutines, degrees ofparallelism in distributing computation tasks to different locations orsubroutines), a definition of which features of the installation areenabled and which features of the installation are disabled.

FIG. 11 is a flowchart of an example method 1100 for operating aninstallation of a software program (e.g. the first installation of asoftware program 504 and/or the second installation of a softwareprogram 506). In some embodiments, one or more of the functionsdiscussed below with respect to FIG. 11 and method 1100 is performed byhardware processing circuitry. For example, in some embodiments,instructions (e.g. instructions 1324, discussed below) stored in amemory (e.g. memory 1304 and/or memory 1306 discussed below) configurehardware processing circuitry (e.g. the hardware processor 1302,discussed below) to perform one or more of the functions discussed belowwith respect to FIG. 11 . In some embodiments, the method 1100 isperformed by the first installation of a software program 504 and/or thesecond installation of a software program 506).

After start operation 1105, method 1100 moves to operation 1110, where amodel is received or otherwise obtained. The model is obtained, in someembodiments, from a product management system, such as the productmanagement system 502, discussed above with respect to FIG. 5 . Themodel is trained to select one of a predefined set of operating statesbased on operating parameters of an installation of a software program.In some embodiments, the plurality of operating parameters include alist of applications installed on the installation of a softwareprogram, a number of configured and/or active users, a number of searchheads, one or more resource utilization measurements, such as CPUutilization, memory utilization, or a rate of access of log data. Insome embodiments, the plurality of operating parameters includes one ormore performance metrics, such as one or more latency metrics, or one ormore throughput metrics. In some embodiments, the plurality of operatingparameters include configuration parameters, such as a number ofcomputer processors, a number of computing instances, a number ofprocessing threads, a number of computing instances, a memory size, aswap space size, available disk memory, available swap memory, or otherconfiguration parameters. Other operational parameter values associatedwith each of the predefined states is discussed above, for example, withrespect to FIG. 10B.

In operation 1130, operating parameters of the installation of asoftware program are collected or measured. For example, in embodimentsthat include resource utilization and/or performance metrics in theirset of operating parameters, operation 1130 includes, in someembodiments, recording values of these parameters. A list ofapplications installed on the installation of a software program issimilarly updated and/or recorded in some embodiments. In someembodiments, collecting or measuring operating parameters includescopying or storing configuration files that define one or more operatingparameter values.

In some embodiments, operation 1130 includes collecting or measuring oneor more of, during a predefined time period, a number of active users(e.g. users who have logged in within a predefined time period), anumber of active users having each of a plurality of differentpredefined sets of access privileges or capabilities, workloadcharacteristics of the installation, CPU utilization, memoryutilization, utilization of storage media, available read or writebandwidth of storage media, network utilization, a rate of ingestion oflog data, a rate of access of log data, a profile of ingested log datacategories, a profile of accessed log data categories, a rate at whicheach of a plurality of log data categories has been accessed, a numberof each of a plurality of different types of computations initiated by auser, a data access latency, a data access success rate, a responselatency to user actions, a computation success rate of user initiatedactions, a computation latency, errors, one or more queueing delaymetrics, a response rate of automated computations, a success rate ofautomated computations, a computation latency of automated computations,one or more error metrics or error status codes of automatedcomputations, one or more queueing delay measurements of automatedcomputations, a system uptime measurement, a system availabilitymeasurement, a measurement of amounts of user generated computations ofdifferent types, number of automated computations of different types,number of data sources, number of user-created indexes or otherorganization of data, a number of page views, a number of sessions, anumber of applications installed, an identification of features accessedduring a predefined time period, a number of accesses to each of one ormore features, a virtual compute utilization (e.g., a metric combiningCPU, memory, disk, and network utilization metrics), a number or type ofdevices connecting to the installation, a number or type ofvisualizations viewed within a predefined time period, a number orlocation of physical sites at which the software is deployed, a listand/or versions of applications installed.

In operation 1140, the collected or measured operating parameters ofoperation 1130 are provided to the model received or obtained inoperation 1110.

In operation 1150, a selection of one of the predefined states isreceived from the model. For example, as discussed above with respect toFIG. 9 , in some embodiments, the model 718 provides the operating stateindicator 920 based on one or more of operating parameter values 910(e.g. measured or collected operating parameters of operation 1130), acustomer identifier 912, and an indication of an existing or currentoperating state 914.

In operation 1160, at least one operating parameter of the installationof a software program is adjusted based on the selected one predefinedstate. Thus, for example, if the one predefined state indicates aparticular set of applications are installed on an installation of asoftware program operating in the one predefined state (e.g. networkmonitoring or user behavior monitoring), operation 1160 installs anyapplications included in the particular set of applications that are notcurrently installed (e.g. firewall monitoring), and uninstalls anyapplications that are currently installed (and included within a domainof applications controlled or managed by the disclosed embodiments) butare not included in the one predefined state. As another example, theone predefined state defines, in some embodiments, a number of searchheads or a number of process threads allocated for certain tasksperformed by the installation of a software program. Thus, operation1160 includes, in some embodiments, spawning new processing threadsand/or deleting processing threads such that a number of processingthreads in use by the installation of a software program is consistentwith thread specifications indicated by the one predefined state.

As an additional example, in some embodiments, a predefined stateselected by the model is associated with, or defines a set of automatedcomputations to generate a set of customized reports and alerts. Thus,operation 1160 includes, in some embodiments, the actual and automatedscheduling and execution of the required computations, the actual andautomated generation of customized reports, and the actual and automatedsending of alert notifications via email or other integratedcommunication systems.

As a further example, a predefined state defines, in some embodiments, aset of software features and capabilities to be highlighted. Thus,operation 1160 includes, in some embodiments, automated highlighting ofcertain software features and capabilities, and the automated disablingof certain software features and capabilities, either through userdialog and other user interface communications, or silently withoutnotifying the user.

After operation 1160 completes, method 1100 moves to end operation 1170.

In some embodiments, method 1100 includes periodically receiving orotherwise obtaining an update to the model that is local to theinstallation of the software program from the product management system.The updated local model is then used to obtain a recommended operatingstate for the installation of a software program as described above. Insome embodiments, the receiving of an updated model is not necessarilyperiodic, but is obtained asynchronously at unpredictable timeintervals.

Some embodiments of method 1100 include transmitting any one or more ofthe operating parameters discussed above to the product managementsystem. The product management system, as described above, utilizes thistransmitted information, in at least some embodiments to train anupdated model for later deployment to one or more installations of thesoftware program. As discussed above, in some embodiments, the updatedmodel is also trained based on CRM information obtained from a CRMsystem, such as the CRM system 508, discussed above with respect to FIG.5 .

In some embodiments, the installation of a software program (e.g. thefirst installation of a software program 504 and/or the secondinstallation of a software program 506) becomes disconnected orotherwise unable to communicate with the product management system (e.g.the product management system 502). In this case, the installation of asoftware program relies on the received model (of operation 1110) forperhaps an extended period of time until communication is reestablishedand/or an updated model is available from the product management system.Thus, in some embodiments, a local model drives an installation of asoftware program through a series of state changes and productconfiguration modifications without any further input being providedfrom the installation of a software program.

FIG. 12 is a flowchart of an example method 1200 for training andproviding a model to an installation of a software program (e.g. thefirst installation of a software program 504 and/or the secondinstallation of a software program 506). In some embodiments, one ormore of the functions discussed below with respect to FIG. 12 and method1200 is performed by hardware processing circuitry. For example, in someembodiments, instructions (e.g. instructions 1324, discussed below)stored in a memory (e.g. memory 1304 and/or memory 1306 discussed below)configure hardware processing circuitry (e.g. the hardware processor1302, discussed below) to perform one or more of the functions discussedbelow with respect to FIG. 12 . In some embodiments, the method 1200 isperformed by the product management system 502, discussed above withrespect to FIG. 1 .

After start operation 1205, method 1200 moves to operation 1210, whereCRM parameters of a plurality of customers are obtained. In someembodiments, the CRM parameters are obtained from a CRM system (e.g. theCRM system 508 discussed above with respect to FIG. 5 ). The CRMparameters obtained in operation 1205 include, in some embodiments, oneor more of firmographics parameters, recent purchase informationprevious purchase information, product license information, a size ofthe customer, a number of employees of the customer, an indicator of anindustry of the customer, products purchased by a customer, discountsreceived by the customer, a list of use cases purchased by the customer,a number of people contacts at the customer, a number of support casessubmitted by the customer, a number of buying centers at the customer,or identification of one or more regulatory schemes under which thecustomer operates.

In operation 1220, historical operational parameter values of theplurality of customers are obtained. As discussed above, historicaloperational parameter values include one or more of usage metrics,performance metrics, resource utilization metrics, a list of installedapplications, or other operational parameter values as discussed above.In some embodiments, the historical operation parameter values areobtained from a training database, such as a trained data store asrepresented by historical info 730, discussed above.

In operation 1230, a model is trained based on the obtained CRMinformation and the historical operational parameter values. In someembodiments, the training of the model 718 is also facilitated vialabels associated with the historical operational parameter valuesand/or CRM information of operation 1210. For example, as discussedabove with respect to FIG. 8 , some embodiments label historicalinformation 802 with labels 804 indicating an operating staterecommendation or selection appropriate for the given historicalinformation.

In operation 1240, the trained model is provided to one or moreinstallations of the software program. For example, as discussed abovewith respect to FIG. 5 , in some embodiments, the product managementsystem 502 provides a model to an installation of a software program(e.g., the first installation of a software program 504 and/or thesecond installation of a software program 506) via the first deploymentmessage 516 and/or the second deployment message 518 respectively.

After operation 1240 completes, method 1200 moves to end operation 1250.

FIG. 13 illustrates a block diagram of an example machine 1300 uponwhich any one or more of the techniques (e.g., methodologies) discussedherein may perform. Machine (e.g., computer system) 1300 may include ahardware processor 1302 (e.g., a central processing unit (CPU), agraphics processing unit (GPU), a hardware processor core, or anycombination thereof), a main memory 1304 and a static memory 1306, someor all of which may communicate with each other via an interlink 1308(e.g., bus). In some embodiments, the example machine 1300 isimplemented by an installation of a software program (e.g. one or moreof the installation of a software program 504 or the installation of asoftware program 506) and/or the product management system 502.

Specific examples of main memory 1304 include Random Access Memory(RAM), and semiconductor memory devices, which may include, in someembodiments, storage locations in semiconductors such as registers.Specific examples of static memory 1306 include non-volatile memory,such as semiconductor memory devices (e.g., Electrically ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM)) and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; RAM; andCD-ROM and DVD-ROM disks.

The machine 1300 may further include a display device 1310, an inputdevice 1312 (e.g., a keyboard), and a user interface (UI) navigationdevice 1314 (e.g., a mouse). In an example, the display device 1310,input device 1312 and UI navigation device 1314 may be a touch screendisplay. The machine 1300 may additionally include a mass storage device1316 (e.g., drive unit), a signal generation device 1318 (e.g., aspeaker), a network interface device 1320, and one or more sensors 1321,such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor. The machine 1300 may include an outputcontroller 1328, such as a serial (e.g., universal serial bus (USB),parallel, or other wired or wireless (e.g., infrared (IR), near fieldcommunication (NFC)) connection to communicate or control one or moreperipheral devices (e.g., a printer, card reader). In some embodimentsthe hardware processor 1302 and/or instructions 1324 may compriseprocessing circuitry and/or transceiver circuitry.

The mass storage device 1316 may include a machine readable medium 1322on which is stored one or more sets of data structures or instructions1324 (e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 1324 may alsoreside, completely or at least partially, within the main memory 1304,within static memory 1306, or within the hardware processor 1302 duringexecution thereof by the machine 1300. In an example, one or anycombination of the hardware processor 1302, the main memory 1304, thestatic memory 1306, or the mass storage device 1316 may constitutemachine readable media.

Specific examples of machine readable media may include: non-volatilememory, such as semiconductor memory devices (e.g., EPROM or EEPROM) andflash memory devices; magnetic disks, such as internal hard disks andremovable disks; magneto-optical disks; RANI; and CD-ROM and DVD-ROMdisks.

While the machine readable medium 1322 is illustrated as a singlemedium, the term “machine readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 1324.

An apparatus of the machine 1300 may be one or more of a hardwareprocessor 1302 (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), a hardware processor core, or any combinationthereof), a main memory 1304 and a static memory 1306, sensors 1321,network interface device 1320, antennas 1360, a display device 1310, aninput device 1312, a UI navigation device 1314, a mass storage device1316, instructions 1324, a signal generation device 1318, and an outputcontroller 1328. The apparatus may be configured to perform one or moreof the methods and/or operations disclosed herein. The apparatus may beintended as a component of the machine 1300 to perform one or more ofthe methods and/or operations disclosed herein, and/or to perform aportion of one or more of the methods and/or operations disclosedherein. In some embodiments, the apparatus may include a pin or othermeans to receive power. In some embodiments, the apparatus may includepower conditioning hardware.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 1300 and that cause the machine 1300 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. Specificexamples of machine readable media may include: non-volatile memory,such as semiconductor memory devices (e.g., Electrically ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM)) and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; RandomAccess Memory (RAM); and CD-ROM and DVD-ROM disks. In some examples,machine readable media may include non-transitory machine readablemedia. In some examples, machine readable media may include machinereadable media that is not a transitory propagating signal.

The instructions 1324 may further be transmitted or received over acommunications network 1326 using a transmission medium via the networkinterface device 1320 utilizing any one of a number of transferprotocols (e.g., frame relay, internet protocol (IP), transmissioncontrol protocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP)). Example communication networks may include a localarea network (LAN), a wide area network (WAN), a packet data network(e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®), IEEE 802.15.4 family ofstandards, a Long Term Evolution (LTE) 4G or 5G family of standards, aUniversal Mobile Telecommunications System (UMTS) family of standards,peer-to-peer (P2P) networks, satellite communication networks, amongothers.

In an example, the network interface device 1320 may include one or morephysical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or moreantennas to connect to the communications network 1326. In an example,the network interface device 1320 may include one or more antennas 1360to wirelessly communicate using at least one of single-inputmultiple-output (SIMO), multiple-input multiple-output (MIMO), ormultiple-input single-output (MISO) techniques. In some examples, thenetwork interface device 1320 may wirelessly communicate using MultipleUser MIMO techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 1300, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operations andmay be configured or arranged in a certain manner. In an example,circuits may be arranged (e.g., internally or with respect to externalentities such as other circuits) in a specified manner as a module. Inan example, the whole or part of one or more computer systems (e.g., astandalone, client or server computer system) or one or more hardwareprocessors may be configured by firmware or software (e.g.,instructions, an application portion, or an application) as a modulethat operates to perform specified operations. In an example, thesoftware may reside on a machine readable medium. In an example, thesoftware, when executed by the underlying hardware of the module, causesthe hardware to perform the specified operations.

Accordingly, the term “module” is understood to encompass a tangibleentity, be that an entity that is physically constructed, specificallyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform part or all of any operation described herein. Consideringexamples in which modules are temporarily configured, each of themodules need not be instantiated at any one moment in time. For example,where the modules comprise a general-purpose hardware processorconfigured using software, the general-purpose hardware processor may beconfigured as respective different modules at different times. Softwaremay accordingly configure a hardware processor, for example, toconstitute a particular module at one instance of time and to constitutea different module at a different instance of time.

Some embodiments may be implemented fully or partially in softwareand/or firmware. This software and/or firmware may take the form ofinstructions contained in or on a non-transitory computer-readablestorage medium. Those instructions may then be read and executed by oneor more processors to enable performance of the operations describedherein. The instructions may be in any suitable form, such as but notlimited to source code, compiled code, interpreted code, executablecode, static code, dynamic code, and the like. Such a computer-readablemedium may include any tangible non-transitory medium for storinginformation in a form readable by one or more computers, such as but notlimited to read only memory (ROM); random access memory (RANI); magneticdisk storage media; optical storage media; flash memory, etc.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operations andmay be configured or arranged in a certain manner. In an example,circuits may be arranged (e.g., internally or with respect to externalentities such as other circuits) in a specified manner as a module. Inan example, the whole or part of one or more computer systems (e.g., astandalone, client or server computer system) or one or more hardwareprocessors may be configured by firmware or software (e.g.,instructions, an application portion, or an application) as a modulethat operates to perform specified operations. In an example, thesoftware may reside on a machine readable medium. In an example, thesoftware, when executed by the underlying hardware of the module, causesthe hardware to perform the specified operations.

7.0. Terminology

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor, will cause a computingdevice to execute functions involving the disclosed techniques. In someembodiments, a carrier containing the aforementioned computer programproduct is provided. The carrier is one of an electronic signal, anoptical signal, a radio signal, or a non-transitory computer-readablestorage medium.

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.Furthermore, use of “e.g.,” is to be interpreted as providing anon-limiting example and does not imply that two things are identical ornecessarily equate to each other.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is understood with the context asused in general to convey that an item, term, etc. may be either X, Y orZ, or any combination thereof. Thus, such conjunctive language is notgenerally intended to imply that certain embodiments require at leastone of X, at least one of Y and at least one of Z to each be present.Further, use of the phrase “at least one of X, Y or Z” as used ingeneral is to convey that an item, term, etc. may be either X, Y or Z,or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines or an isolated executionenvironment, rather than in dedicated computer hardware systems and/orcomputing devices. Likewise, the data repositories shown can representphysical and/or logical data storage, including, e.g., storage areanetworks or other distributed storage systems. Moreover, in someembodiments the connections between the components shown representpossible paths of data flow, rather than actual connections betweenhardware. While some examples of possible connections are shown, any ofthe subset of the components shown can communicate with any other subsetof components in various implementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C. sec. 112(f) (MA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

Example 1 is a method implemented using a computing device, comprising:receiving, from a product management system a model trained to selectone of a plurality of predefined states based on a state of aninstallation of a software program on a computing device, each of thepredefined states associated with a configuration of the softwareprogram, each configuration associated with operational parameter valuesof the software program; determining a state of the installation of thesoftware program; inputting the determined state into the model;obtaining, from the model, and based on the determined state, aselection of one of the plurality of predefined states; and adjusting aparameter of the software program according to the selected onepredefined state.

In Example 2, the subject matter of Example 1 optionally includeswherein each configuration is associated with operational parametersincluding a list of limits and capabilities of users of theinstallation, an amount of pre-processing of incoming data, a type ofpre-processing of incoming data, a list of enabled application features,a list of disabled application features, a list of installedapplications, a list of applications that are not installed, or a numberof computing instances within the installation.

In Example 3, the subject matter of any one or more of Examples 1-2optionally include wherein determining the state of the installation ofthe software program includes measuring one or more of a number ofactive users having each of a plurality of different capabilities, anumber of accesses to each of a plurality of features within apredefined time period, a virtual compute utilization, a rate at whicheach of a plurality of log data categories is accessed, or a number ofeach of a plurality of different types of computations initiated by auser within a predefined time period.

In Example 4, the subject matter of any one or more of Examples 1-3optionally include periodically obtaining, from the product managementsystem, by the installation of the software program, an updated model;and adjusting at least one of the parameters of the installation of thesoftware program based on the updated model.

In Example 5, the subject matter of Example 4 optionally includestransmitting, to the product management system, by the software program,usage information of the installation of the software program oroperating parameter values defining an operating environment orconfiguration of the installation of the software product, wherein theupdated model is generated by the product management system based on theusage information.

In Example 6, the subject matter of Example 5 optionally includestransmitting, to the product management system, one or more of resourceutilization information of the installation of the software program, orinstalled application information of the installation of the softwareprogram, wherein the updated model is generated by the productmanagement system based on the resource utilization information orinstalled application information.

In Example 7, the subject matter of any one or more of Examples 5-6optionally include wherein the updated model is generated by the productmanagement system based on customer relationship management informationfrom across a plurality of installations of the software program.

In Example 8, the subject matter of Example 7 optionally includeswherein the customer relationship management information indicates oneor more of recent purchases of a customer, previous purchases of thecustomer, a size of the customer, a number of employees of the customer,an indicator of an industry of the customer.

In Example 9, the subject matter of any one or more of Examples 5-8optionally include updating a local model based on the usageinformation; determining a second state of the installation of thesoftware program; inputting the second state to the local model;obtaining, from the local model, and based on the determined secondstate, a selection of a second one of the plurality of predefinedstates; and adjusting at least one of the parameters of the installationof the software program in accordance to the selected second onepredefined state.

Example 10 is a computing device, comprising: a processor; and anon-transitory computer-readable medium having stored thereoninstructions that, when executed by the processor, cause the processorto perform operations including: receiving, from a product managementsystem a model trained to select one of a plurality of predefined statesbased on a state of an installation of a software program on a computingdevice, each of the predefined states associated with a configuration ofthe software program, each configuration associated with operationalparameter values of the software program; determining a state of theinstallation of the software program; inputting the determined stateinto the model; obtaining, from the model, and based on the determinedstate, a selection of one of the plurality of predefined states; andadjusting a parameter of the software program according to the selectedone predefined state.

In Example 11, the subject matter of Example 10 optionally includeswherein each configuration is associated with operational parametersincluding a list of limits and capabilities of users of theinstallation, an amount of pre-processing of incoming data, a type ofpre-processing of incoming data, a list of enabled application features,a list of disabled application features, a list of installedapplications, a list of applications that are not installed, or a numberof computing instances within the installation.

In Example 12, the subject matter of any one or more of Examples 10-11optionally include wherein determining the state of the installation ofthe software program includes measuring one or more of a number ofactive users having each of a plurality of different capabilities, anumber of accesses to each of a plurality of features within apredefined time period, a virtual compute utilization, a rate at whicheach of a plurality of log data categories is accessed, or a number ofeach of a plurality of different types of computations initiated by auser within a predefined time period.

In Example 13, the subject matter of any one or more of Examples 10-12optionally include the operations further comprising: periodicallyobtaining, from the product management system, by the installation ofthe software program, an updated model; and adjusting at least one ofthe parameters of the installation of the software program based on theupdated model.

In Example 14, the subject matter of Example 13 optionally includes theoperations further comprising transmitting, to the product managementsystem, by the software program, usage information of the installationof the software program or operating parameter values defining anoperating environment or configuration of the installation of thesoftware product, wherein the updated model is generated by the productmanagement system based on the usage information.

In Example 15, the subject matter of Example 14 optionally includes theoperations further comprising transmitting, to the product managementsystem, one or more of resource utilization information of theinstallation of the software program, or installed applicationinformation of the installation of the software program, wherein theupdated model is generated by the product management system based on theresource utilization information or installed application information.

In Example 16, the subject matter of any one or more of Examples 14-15optionally include wherein the updated model is generated by the productmanagement system based on customer relationship management informationfrom across a plurality of installations of the software program.

In Example 17, the subject matter of Example 16 optionally includeswherein the customer relationship management information indicates oneor more of recent purchases of a customer, previous purchases of thecustomer, a size of the customer, a number of employees of the customer,an indicator of an industry of the customer.

Example 18 is a non-transitory computer-readable medium having storedthereon instructions that, when executed by one or more processors,cause the one or more processor to perform operations including:receiving, from a product management system a model trained to selectone of a plurality of predefined states based on a state of aninstallation of a software program on a computing device, each of thepredefined states associated with a configuration of the softwareprogram, each configuration associated with operational parameter valuesof the software program; determining a state of the installation of thesoftware program; inputting the determined state into the model;obtaining, from the model, and based on the determined state, aselection of one of the plurality of predefined states; and adjusting aparameter of the software program according to the selected onepredefined state.

In Example 19, the subject matter of Example 18 optionally includes theoperations further comprising: periodically obtaining, from the productmanagement system, by the installation of the software program, anupdated model; adjusting at least one of the parameters of theinstallation of the software program based on the updated model; andtransmitting, to the product management system, by the software program,usage information of the installation of the software program oroperating parameter values defining an operating environment orconfiguration of the installation of the software product, wherein theupdated model is generated by the product management system based on theusage information.

In Example 20, the subject matter of Example 19 optionally includes theoperations further comprising transmitting, to the product managementsystem, one or more of resource utilization information of theinstallation of the software program, or installed applicationinformation of the installation of the software program, wherein theupdated model is generated by the product management system based on theresource utilization information or installed application information.

In Example 21, the subject matter of any one or more of Examples 19-20optionally include wherein the updated model is generated by the productmanagement system based on customer relationship management informationfrom across a plurality of installations of the software program.

In Example 22, the subject matter of any one or more of Examples 19-21optionally include the operations further comprising: updating a localmodel based on the usage information; determining a second state of theinstallation of the software program; inputting the second state to thelocal model; obtaining, from the local model, and based on thedetermined second state, a selection of a second one of the plurality ofpredefined states; and adjusting at least one of the parameters of theinstallation of the software program in accordance to the selectedsecond one predefined state.

What is claimed is:
 1. A system comprising: at least one processor; anda non-transitory computer-readable medium having instructions that, whenexecuted, cause the at least one processor to perform operationscomprising: obtaining historical information and a label, the historicalinformation comprising customer resource management parameters andhistorical operational parameter values for a plurality of customers,the plurality of customers including first and second customers;training a model based on the historical information to generate atrained model; and providing the trained model to installations of asoftware program hosted by a plurality of devices being utilized by theplurality of customers, the providing the trained model comprisingproviding the trained model to a first installation of the softwareprogram being hosted by a first device being utilized by the firstcustomer and providing the trained model to a second installation of thesoftware program being hosted by a second device being utilized by thesecond customer.
 2. The system of claim 1, wherein the operationsfurther comprise preparing information to generate the historicalinformation, wherein the preparing the information is performed beforethe obtaining the historical information.
 3. The system of claim 2,wherein the preparing the information includes: identifying a pluralityof streams of customer information based on a plurality of customeridentifiers; and dividing the plurality of streams of customerinformation, based on a single time period, to prepare the information.4. The system of claim 3, wherein the plurality of customer identifiersincludes a first customer identifier identifying the first customer anda second customer identifier identifying the second customer.
 5. Thesystem of claim 1, wherein the historical information further comprisesoperating state information that includes a plurality of operatingstates.
 6. The system of claim 5, wherein the plurality of operatingstates includes a first operating state defining a plurality ofparameters, and wherein an installation of a software program operatesbased on the first operating state and the plurality of parameters. 7.The system of claim 5, wherein the plurality of operating statesincludes a second operating state defining a plurality of sub-states,and wherein an installation of a software program operates based on thesecond operating state and the plurality of sub-states.
 8. The system ofclaim 6, wherein the plurality of parameters are read-only parametervalues that cannot be directly modified by the trained model.
 9. Thesystem of claim 5, wherein the training the model further comprises:selecting the label, as an operating state, from the plurality ofoperating states; and labeling the trained model with the label based onthe historical information.
 10. A method implemented using a computingdevice, the method comprising: obtaining historical information and alabel, the historical information comprising customer resourcemanagement parameters and historical operational parameter values for aplurality of customers, the plurality of customers including first andsecond customers; training a model based on the historical informationto generate a trained model; and providing the trained model toinstallations of a software program hosted by a plurality of devicesbeing utilized by the plurality of customers, the providing the trainedmodel comprising providing the trained model to a first installation ofthe software program being hosted by the first device being utilized bya first customer and providing the trained model to a secondinstallation of the software program being hosted by a second devicebeing utilized by the second customer.
 11. The method of claim 10,wherein the customer resource management parameters include a firstcustomer resource management parameter describing the first customer anda second customer resource management parameter describing the secondcustomer.
 12. The method of claim 11, wherein the first customerresource management parameter is selected from a group of customerresource management parameters including a size of the first customer, anumber of employees of the first customer, an indicator of an industryof the first customer, and an identification of one or more regulatoryschemes under which the first customer operates.
 13. The method of claim12, wherein the second customer resource management parameter isselected from a group of customer resource management parametersincluding recent purchase information, previous purchase information,and product license information.
 14. The method of claim 10, wherein thehistorical operational parameter values include a first operationalparameter value describing the first customer and a second operationalparameter value describing the second customer.
 15. The method of claim14, wherein the first operational parameter value is selected from agroup of operational parameter values including a usage metric and aperformance metric.
 16. The method of claim 14, wherein the secondoperational parameter value is selected from a group of operationalparameter values including a resource utilization metric and a list ofinstalled applications.
 17. The method of claim 10, wherein theobtaining the historical operational parameter values includes obtainingthe values from a training database.
 18. The method of claim 10, whereinthe providing the trained model to installations of the software programincludes communicating the trained model, with a deployment message, tothe first device hosting the first installation of the software programand communicating the trained model, with a deployment message, to thesecond device hosting the second installation of the software program.19. A non-transitory machine-readable medium and storing a set ofinstructions that, when executed by a processor, causes a machine toperform operations comprising: obtaining historical information and alabel, the historical information comprising customer resourcemanagement parameters and historical operational parameter values for aplurality of customers, the plurality of customers including first andsecond customers; training a model based on the historical informationto generate a trained model; and providing the trained model toinstallations of a software program hosted by a plurality of devicesbeing utilized by the plurality of customers, the providing the trainedmodel comprising providing the trained model to a first installation ofthe software program being hosted by the first device being utilized bya first customer and providing the trained model to a secondinstallation of the software program being hosted by a second devicebeing utilized by the second customer.
 20. The non-transitorymachine-readable medium of claim 19, wherein the operations furthercomprise: preparing information to generate the historical information,wherein the preparing the information is performed before the obtainingthe historical information, the preparing the information includes:identifying a plurality of streams of customer information based on aplurality of customer identifiers, and dividing the plurality of streamsof customer information, based on a single time period, to yieldhistorical information.